{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TimeSeries_Validation_ModelBuilding.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOd5jl/L3jzL+9GzMkcI4oW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rjrahul24/ai-with-python-series/blob/main/05.%20Build%20Concrete%20Time%20Series%20Forecasts/Colab%20Notebook/TimeSeries_Validation_ModelBuilding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "-XdYB5-wH4vO",
        "outputId": "c168c976-14a4-4e93-d22e-c99df6717765"
      },
      "source": [
        "# Importing the basic preprocessing packages\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "# The series module from Pandas will help in creating a time series\n",
        "from pandas import Series,DataFrame\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "# About the Data Set (Location: https://www.kaggle.com/sumanthvrao/daily-climate-time-series-data) \n",
        "# To forecast the daily climate of a city in India\n",
        "time_series = pd.read_csv('https://raw.githubusercontent.com/rjrahul24/ai-with-python-series/main/05.%20Build%20Concrete%20Time%20Series%20Forecasts/Data/DailyDelhiClimateTrain.csv', parse_dates=['date'], index_col='date')\n",
        "time_series.head()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meantemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>meanpressure</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-01-01</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>84.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1015.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-02</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>2.980000</td>\n",
              "      <td>1017.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-03</th>\n",
              "      <td>7.166667</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>4.633333</td>\n",
              "      <td>1018.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-04</th>\n",
              "      <td>8.666667</td>\n",
              "      <td>71.333333</td>\n",
              "      <td>1.233333</td>\n",
              "      <td>1017.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-05</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>86.833333</td>\n",
              "      <td>3.700000</td>\n",
              "      <td>1016.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             meantemp   humidity  wind_speed  meanpressure\n",
              "date                                                      \n",
              "2013-01-01  10.000000  84.500000    0.000000   1015.666667\n",
              "2013-01-02   7.400000  92.000000    2.980000   1017.800000\n",
              "2013-01-03   7.166667  87.000000    4.633333   1018.666667\n",
              "2013-01-04   8.666667  71.333333    1.233333   1017.166667\n",
              "2013-01-05   6.000000  86.833333    3.700000   1016.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "9TUSLkpyI0QY",
        "outputId": "74737bdb-8641-4213-9860-34cd7c67c5eb"
      },
      "source": [
        "# Below are a few statistical methods on time series that will help in understanding the data patterns\n",
        "# Plotting all the individual columns to observe the pattern of data in each column\n",
        "time_series.plot(subplots=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f91bd50fbd0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f91bd4da890>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f91bd4991d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f91bd452510>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vY_a9knI35C",
        "outputId": "2eef1ffd-cf47-48b7-cf4a-7e80876d2dc7"
      },
      "source": [
        "# Calculating the mean, maximum values, and minimum of all individual columns of the dataset\n",
        "time_series.mean()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "meantemp          25.495521\n",
              "humidity          60.771702\n",
              "wind_speed         6.802209\n",
              "meanpressure    1011.104548\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrIexlRjI6x3",
        "outputId": "14b7440c-322c-471d-8645-bcbdfe781b8d"
      },
      "source": [
        "time_series.max()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "meantemp          38.714286\n",
              "humidity         100.000000\n",
              "wind_speed        42.220000\n",
              "meanpressure    7679.333333\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yFx0Dr51I_W0",
        "outputId": "a63d73db-e42d-45f1-ceff-2a49f9e3f542"
      },
      "source": [
        "time_series.min()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "meantemp         6.000000\n",
              "humidity        13.428571\n",
              "wind_speed       0.000000\n",
              "meanpressure    -3.041667\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "rPcfadYcJKew",
        "outputId": "506c6474-42db-4df7-ffb4-8acf0daed200"
      },
      "source": [
        "# The describe() method gives information like count, mean, deviations and quartiles of all columns\n",
        "time_series.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meantemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>meanpressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1462.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25.495521</td>\n",
              "      <td>60.771702</td>\n",
              "      <td>6.802209</td>\n",
              "      <td>1011.104548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.348103</td>\n",
              "      <td>16.769652</td>\n",
              "      <td>4.561602</td>\n",
              "      <td>180.231668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>13.428571</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.857143</td>\n",
              "      <td>50.375000</td>\n",
              "      <td>3.475000</td>\n",
              "      <td>1001.580357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27.714286</td>\n",
              "      <td>62.625000</td>\n",
              "      <td>6.221667</td>\n",
              "      <td>1008.563492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>31.305804</td>\n",
              "      <td>72.218750</td>\n",
              "      <td>9.238235</td>\n",
              "      <td>1014.944901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>38.714286</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>42.220000</td>\n",
              "      <td>7679.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          meantemp     humidity   wind_speed  meanpressure\n",
              "count  1462.000000  1462.000000  1462.000000   1462.000000\n",
              "mean     25.495521    60.771702     6.802209   1011.104548\n",
              "std       7.348103    16.769652     4.561602    180.231668\n",
              "min       6.000000    13.428571     0.000000     -3.041667\n",
              "25%      18.857143    50.375000     3.475000   1001.580357\n",
              "50%      27.714286    62.625000     6.221667   1008.563492\n",
              "75%      31.305804    72.218750     9.238235   1014.944901\n",
              "max      38.714286   100.000000    42.220000   7679.333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "fTlunXRVL0OV",
        "outputId": "81171c9c-4a12-49de-d688-67e766bcc275"
      },
      "source": [
        "timeseries_mm = time_series['wind_speed']\n",
        "timeseries_mm.plot(style='g--')\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BIJFGH7YmvzwfQOXurDZQ27hdzBQUQ8UVxaqeh8GoTeza6L/V8y0AdadRSF5ZHrR6raqllc+mnsWhhEMI9Q6Fk4MTOtTvoNq5qoJabh1CxMIIcHM5qs0HKgOsH+77UJVzHk44DAA0+7muQL6Xh1s8bOORMOwRuzb6jpxjnepPOqrFKABCf1I1KdWUQs/rodVr75uuTmQiFStpyEqf7DKsjb+7v+Q8dQ3WRIWhhF0b/fqe9fF8x+cR6h1q66HYDY6cI97t+y4upF8AAGy9utXGI6pdatMAT+s1rdbOZU0cOUeMbz+exiTqKuXacloKg2E97Nron08/jxVnV9SZ1eyPZ35U/RxyzXifiD6qn9MSwnyE9o3N/Zureh7x3+/rJih6+kb0rbVz1gUcHRxxLesaLQ1dV3l9x+tovbS1qu1H70fsWqd/MvkkAKH3rFoKDWtyO++26ufQ83p8dvgzPNHmCQDAIy0fUf2cltA9tDt0H+lU9+2LcXNyQxO/Jmji30SV46+OXa3KcdVGz+sRmx5b54QQcvbf2g8AKCwvRD2PejYezb2DXa/0iRFVy2d7L2AvDT4qdBVIKUxRrSIlKTAnnlTKteVwdXJVrdwAKWZX37O+KsdXiwpdBbR6LU6nnLb1UGrE691eB1D38iTsHbs2+oS6Ekgjjai7hnRV7RyNfBuhf8P+9PE7e95R7VxVYd+tfYhYGIFPD32qyvETpyaCn83D1amyX21OaQ6uZl1FM/9mqpzT28UbjpxjnW+QXlch37ujQ81qH0XFReHInSPWGNI9gV1fzeIbvC7g5OAEF0eXGl+kpqjvWR+eLp509dksQB2DV1VKtaUAalmnr7KvPSY9Bjpeh4LygjrhXiTUlUWSOTydPeHl4iWR6VaHEetGAAD42ffG91JT7HqlT9QTajYlsSZFFUWo0FWo6ts/lXIKe+P3ItQ7FIHugWhdT90uXZaiti/f+wtvcHM5SfE9Ytxe3/G6Kuc8lXwKAFBQXqDK8dVmXLtxth5CjeDB03uqJvi6+qJFYAsrjaruY9dG39XJFYHugbSipL1DdPpqNlEBAHdnd2j1WuSU5qBMW6bquewFUn9JvIpVe6VP2iXW1ZVzp+BOth5CjUgvSgcg1BKqCS3rtUQTP3WC/XURs9aU47gIjuP2cxx3meO4SxzHvXX3+QCO4/ZwHHfj7v/9rT24Cl0FhjUbpnrP2brG2z3fxvn08+DB3xPNr1t91woz/51p0XtrUz753x7/rbVzWRNnR2dM6DChTjZ0F7MmVuh9XNOVfnRyNHbH7bbGkO4JLFlCawFM53m+DYBeAKZwHNcGwPsA9vI8Hwlg793HVuVixkVsuLihzqy0Fp9cDED9lSHHcfQcI5uPVPVclhIZKNTRbxFQ9W30texr+PLolxa9V/zdkuSjLiFdqnzOqlDXdPpODk64kXMDf13/y9ZDYdghZgO5PM+nAki9++9CjuOuAAgD8AiAQXfftgbAAQDvWXNwZ1LPAABu5tysE6t9tcsvEIM39+BcWlfl0VaPqnpOS2lXv12tB8rcnd3RIbgDInwiVDn+0lNLVTmu2uj0OpxIOoE2QW1sPRS7oENwBzT1b2rrYdgNVXKWcxzXGEBnACcBBN+dEAAgDYBilxOO4yZyHHea47jTmZmZVRoc8enlluVW6XO2pqorwwfXPQjPz6vWj5Wcw16KgRVXFONK5hXVupwRAyZWRpFzNQ9QJwuYSGPDvMNUOb5akDjP5czLNh5JzXip80sAUONJXc/razVp0N6x2OhzHOcF4A8Ab/M8L5Ez8MISVNHS8Ty/nOf5bjzPdwsKCqrRYO2d4c2Gw93JvcqlETycPRDkYdl30yG4g6Q94IyoGVU6l1rsu7UPbZa1UU2nf2nyJfCzedoiEQCySrJwPv28aitaVydXuDm5qSrBrSlv/fMWBq8ZLHmurrmjjEF2tjXNLL6YcRFbrm6xxpDuCSwy+hzHOUMw+L/yPP/n3afTOY4Luft6CIAMaw+OaKPrik+/urg4uliUk8BxHDydPeHs4ExXny3rtVR7eIq8sfMNvP53pVSSZAZXd3Xp7eJd5c+ofV2cTT2LMm0ZckpzVD1PTdh6bSsO3D5g62GoQoh3CDydPRW//+KKYqyNXYsb2TdsMDL12Ru/F6vOrVLl2JaodzgAKwFc4Xl+geilvwA8f/ffzwPYZu3BzegjrGLrysqlQleBUm0pbubcrNLnNl7aaFF/XT2vx/Gk49gTvwch3iEI9Q5VLRvVHEcTj+LwncP0cU1WY4+1fgzLRi0z+R5uLgduLifRzJPr4uW/Xq72uU1xPv08ACC/zH7LgNzJv2PwHJkMX+6szvdSW7g6uqJYU4zC8koX5u6bu/HZoc+QWJCI57c+jyXRS8wep7FfY9X6KKvF0F+GqnZdW7LS7wtgAoAHOI6LufvfQwC+BDCM47gbAIbefWxVPJw90NivcZ2pqT8qUtDpk1iEtSE3c6h3KMq15UgpTFHNh26OAPcA+LtZR6X7x1N/4NkOz1r0Xj2vp/9We6VvL13Jqou9JO5Vl1t5twBIf/N/4//FJ4c+oRPB8aTjZo/T0Lchgj0VQ452y9hWY1Vb0Fmi3jkCGI2CDLHucKTE5cShQ3AH9AjroeZp6hyvd30dsemxAID9t/fbZAz7bu2z2rGG/zIcDpwDdj27y2rHtAZTuk/BC9teqPWdplavxeeHP8e03tOqpbV3c3LDS51esqhm0KZLm3A+/Tw+eeCT6gxVVVbHrAYg3ekviV6Ccl05DVZbMvEfSjikyvjUhOM41SoR2HWq69Wsq9hxfYeth2GSFWdW0JU90ZpX1UiMbTUW7eq3M/s+ctxSbSm92B9v/XiVzqUWpBl8dYKqe+L3WJw8I77JScasWjp9W5UmPpxwGLMPzMaWK9ULPjo7OuNGzg1svWa+wc5Tm5/Cp4fVCb7XFPL9i1f6pIrrvV55988rf6pWx8quC65dyLgAHa/D2dSzqifgVIf0onRM/HsirmRdwYIRC5Bdkl2t4/DgqyQp++LIF7SOPnEp2Zom/k1qXafv5uSGbqHdLFY+VZWvjn6lynHN4ekiyHeTC5PNvndMyzFIyEuQPKfRaXD4zmE08m2kyvhqC7JTUbo3qlJSvHlAc+YtEGHXK32iz6+uMVUbclES3y95XNVaQVuvbsWFjAtV+gxZ9WeWVC33wVqEeIVIiljllubiVPIpi2IMWSVZNNhtqV+e9Ht1dnSmzxWWF0Kj01i0S6oOQ5oMgZODU6379sl3MnOv+dIUC0csxPrH10ueI79BQn6C0kckWCsuowaPt34cLo4uNNtbDOmhMLrFaADApYxLWBOzRrFkQ13U6T/X8TnVJm27NvoEe1XvkHEVVxQDAAY1HgRPZ88q6/Sf7fCsRRmDTg5O6BvRFw80eYA+996/Vk2CtpgI3whJEasDtw+gx089MHv/bLOyzeaLmyNyiXAjm/pteZ6nxzr+8nHws3mJjzujOAOx6bFoX799Tf4Uozg6OMLNya3WC/5V5Xpff2E9Bq0ZVO1z5byXY7clh3necAc8pMkQtKvfDl1DuyJxaiJV+D3+++N4YdsLilnx8bnx+PXCr6qMsUJXgRlRM5Bbat0EUg6canbPro0+CWSIfXr2BNmBWKOtnqUrXuLnJKtPW0nR5EWsyAX6zfFv0HZZW5MNrcX+WPJ3uzoa5il8f/p7tF3WFgdvH1Q8DjmnWj2UT6WcQlFFETKKq5eCklyQDJ1eWiEyvyzf7PVcFVXSlqtbkFGcUavVR2uL1kGt4eLogsT8RPpci8AW6BLSBUUVRdh2dRs18sTXX9u24kzKGcw/Ph9/XbNunaM1sWsU5bjWwG6NflZJFv6N/xdA5Q95K/cWfrvwW7WOF50cje3X1G0UzfM8ijVCOYKqsO78OipPM4VWr8WRO0ew79Y+NPBqgEa+jdDQt2F1h1sjBjcejLZBbeljuaEq1xpvmyiWEnIch2c7PIsNT2wweN+ZlMraS0Snr+TqU0vPfC3rGoDq6fSTC5IRvjAc/9v/P/pcflk+/Ob54YO9H5j8LNn1hXqHmj0PqU+lZOze7vm22c+T79Ue8XLxQmFFoWSR8FLnlxDgFoCYtBi88c8btMihKfdN5wadVZOvuju7A0CdarJjt4FccaCGbOl7/tQTmSWZGN9+fJWP9/3p77Hv1j483PJhq42R/OAPNBbcLQ9FPoQFJxYgqyTLaucQQ27syIBIFFcUIyE/QZK4Upu4O7tLJGVyo2Nqtdknog+9kR04B6x5dI3Bipi8BkhVNLWp028d1BqZCZnVWjmTJLL43Hj6HJEZmpNhBnsFI+udrCqdV6vXGpSLaORXtwO5xLUn/s2vZl3FopOLaIxHrtNXuiaCPINUS7DLK8sDYFnQvSr0b9hftfIfdrvSF8/cAxoNAFAZtKzOzZ5dko2kgiTrDO4uJGmsY4OOVj2uMcjf/UKnF6hO/1jisVo5t5ydN3bSVSZgaOTJKlmJab2n4c+n/qSPJ2yZgNZLDVdiM/rMwOyBs/Fg8wetMOKq81rX16r9WX93IUAqrpNEmoGYa7ReWF6I23m3TSYlcnM5TN4xmT4WL5I8nT3xetfXa9xmsDY4mXQS3FzOQIEEAD/H/AxAer+/tE0owkauN3l9HqWJMiouCieTT1p34He5lSvs0MX3gjXQ8To4cveZ0Veqt0HKCVfHb7f9+nar+/vI6nRJ9BIUVxRj9oHZAKruUx0VOapKklTxd/Ncx+eqdC61ICsvgqnv+rPDn+GZP58BIATCfrvwG+Jy4wze17JeS8wZNEfi5hB/txG+EXB3cle/nn4NdhTiz5KVPnFbiUktTMWJpBPQ6DQ4l3YO3VZ0owaOsPnyZkQuiaTX3fenv6eviYPNRKe/7ZrVK6NYHbIrTi82zGIn3534NyfxG/lv8uWQL9ElpItdq5GqwrHEY9h7a68qx7Zbo08CMwBoIG9a72n4sP+HthqSAeRGu5x5GXfy79CWflXFnE7/k4OfYE3MGnrxzz8+n170Q5sOrdY5rU1D34bgZ/N4pfMrACpdX0qIjbypyeFwwmEMXTtU4iIR4+bkhs4hnVW70T85VP0sVRJg/OX8L/Q5Euc4mGAYmH54/cPovbK3pG7TH1f+kLwnLicON3Nu0nsjwicCI5qNQI+wHpLvu0JXgb239lpUz8nWkEKDSjEg4j5UyiyWXzdPtn0SZyaeQaBHoMF7Q71DVatDRBIESXJiXcBujb74h84uFYJ3zfybYWzrsXZT6lbsm80qyaLb8arWCpK7SuR8dOAjTN09VfE1tSL8lkJ8mmlFadh3ax9ddVt6E5haRa+KWYW9t/biwO0DGN5sOABI4gi5pbkorihG99Du1R2+SYY1HQY/N79qVTKt51EPgLToWYB7AADlyqjFGkH2y4M3+p2QHAWtXgtnB6El4qpHVmHzk5sl7yOLD0tyOIje3VaQEglXsgzFD8ObDYe/m79iHgbpoUA6x51OOY3vor9TjA3p9Loa99k1BvlNIwMMcwlqwqtdXkUDrwZWPSbBbo2+OBpOboJ5R+eh6/Ku1dpuP9X2KcljPa9HYn5ijQKh4guJB4++EX3h4+pj4Oowx4udXjTbKCK3LBduTm4Y2GggBjYaSJ83pwRRm2f/fBbrzq/D3vi9GLJ2CD7YJ4zH0hIGlrjCOHDY/exu8LN5yXWRXpyO2PRY1WIqSjrxqnwWkH4PwV7B4MApToj0/Sb02WTXoNVrsWP8DrzQ6QX8ff1vNF/SXFLkryr3R8r0FJvq9MkEpbRL1vN6g+uodb3WeLLNk+gR1gN57+Xho4EfAQDG/zEeb/7zpqIKLr04ndbxsTZkhyKu/moNOHCqCRXs1uiLt2/k36R9HVkVVYVWgYKenXyRpZpSNFzUUOIXrSrVvdGUqEocgAdPVzq2bom348YOTNgygY6frPyNuWTkkO+tOoXFyGfVUmZEp0Qjtyy3Wm0wSUczcS6DVq8FD14xa5R8f+L+x3JI7oNWr8WkHZPwzbFvsPHSRlToKuw2l8UcZEevFHTuG9EXOr1OIgroEtIFbYLaILMkEz+c/gG3824DqLQRtd17g+w8rd3BbvnZ5YpxDmtgt0ZffGPIL+jqXODOjuND7fkAACAASURBVM4SJQUJCIl9rjVBz+vh4uiCgvICXEivWkmFn2N+tkhZVK4tx8GEgziUcAjBXsGIDIi0SMutBp7O0vaO8pvN2cEZxugZ1pP+28nBCZO6TZKoeZSOSfTkaUVpBu97fcfrBs9ZAzJxVWdSIdevuMnHySRBQbInfo/Rz3Hg6EQu136T70Oj0yAuNw4x6TG02qlEynp3ArEk/mVrnT7Rzz8U+ZDBa35ufsgvz6cLCQCY3ns6kgqScDLpJN7f+z6+PfktANM7y6FNh6q2OCKLFV9XX1WOrwZ2a/TF/ix5cMb3S18M+HmAxccq05Zhd9xuZBRnVEq77t5ANZFxin33Tf2bYmSk4F8kMQhrQ9xJnRt0RkF5AW7k3JDcEJby9/W/0WNFD0X/JwAcuXPEbKyAZAS3q98OnRp0qtLE3DeiL3VnuTq5Yv7w+egW2s3gfSRLVxzDqc3MUxKfqM55SCcwccY0WWjMGTjH4P3Te0+Hi6ML/Nz8EOwVjJIPSpA6PVXyHnJ9ET9ydHI0fU3JZx3sZf815MlKX9wGk3Aq5RQA6bWUkJ+AledWUgnmiaQTks8o/VYezh4mFyE1gWRrJxZUZg2/tv01fHzw4xodt0VgC/yn7X9qdAxj2K3RF/tSiUJF/NzhO4ex6+YuDF4zWHH1J2bDxQ04cucILmdeNmroqgMxRuPbj8fa2LWqNaIO8QqhqhgAGNduHGLSYgBUTx88/o/xOJVyyqibrP/P/dF4UWOTxyBlXxv6NkSLwBYGN5upZJXx7cfjl7HCDovneXyw9wOELahsPr7p0iZ8uO9DzOw/E98M+8ZmCqWaKD7IQkW8qyGr/wD3APx55U9JhvjErhNR/mE5gr2CkVOag5PJJw1cHvXc6yHEK8SsmsXX1Rdv9XyL+pkvZVyq1uKgNiCqI6V7h+r0RdfW2I1jAQiyX/FrxDaQRUFheSEWnVgEnV6Hv679RfNarA3ZDYrLIC8/uxwbL22s0XF1et39l5yl5Ed9sdOLksfpRek4cPuA2cqO4oYf1YkHGINMIPtv7cdHBz7ClJ1TAFTdrzi06VD0Du9t9PVzr53D18O/VtydTOo2qarDplULTRUSs1Sx4uLoAg9njyoZ5s+PfE6/q4LyAiw6uQil2lL6+v7b+/HZ4c/QyLcRpveZblSn3zygOcJ9wtG5QWeLz22MzOJMo4uHmviJxStwYsQXRy/G478/jjEbxtDX4nLisCduD3iex+mU0xi8ZjD+s1m60ivRliC1KFWSp+HuJBhNcUzE2dEZ17KvYetVoZ5+u+/bod+qftX+G9Skf8P+AKQrZTmWfP/LRi3D6Baj4efmBwB4ZfsrmLp7qkWdtdTA0gXg7bzbOHrnqMHzcblx1S45Yw67NfpinT5ZEU3pMQXzhs6jz++4ITRYMVV6Oac0R5KtSG48ssIgF111IDdaalGqmXeahud5kz7J5WeW46ezP9HHS6KX0BuBZCtXhXFtxwEwXq8k0D2QlpYwx9arW3E88TjCfcLBz+bxRJsnAIDefEqIG0QobcfXxq4FALz5z5tos7SN0aCwi6MLmgc0r1YQWM7knZPx4jbpomLWvlkADCfHUk0pSjQlyC3NVTRIOr2O9tfdcrWyEQpZ6f9+6XcAlbuA3NJcjPx1JIavG4643Dh6THljmbGthFUucb1FBkSiR1gPDGg0gEpEyfh23dwlkUGq1ZCjppDVrJI7kLh8LOkgNbTpUGx/ejuCvYKRWZxJv2MfVx/4u/njzR5vWnHUlRAXWq+wqin2CG/tegv9fq7dCdlua++IbzRSV9/LxQt9I/rS5+UV9uTcyb+DRouk9UfIBODm5Ibuod1r1ITE29Vb8XlTiUlKmMu8++iAIEub3H2ywWvXsq5JSi0Tpu6aikCPQHw4wDCY1yG4A17p/IrR7WN2aTZSiixXrFzLvoZbubdwNvUsNl8WNONit4YplIwm2Y0tP7McGr0GhxIO4fHWj2PL1S2SAHJmcSZyS3NppnZNyCvLM9gxDm0yFPll+WgdJC0RUf+b+lRi+PWwr2l5X8KUnVPw45kfAQATu0ykz8srotbzqAedXoeArwIsGiNx65Dr/dUur+LFzi9Co9NAz+vpPUOUJGIZpLHFTRO/JhYV+1OLTZc2ARAykuX0CuuF/LJ8dA3tavTzw5oOAyCU9j54+yD+N/B/uJhxkb7eIbgDCisKa6SuOZNyBjx4xbgTCbY3C6heP9tA90CE+4QbPP9mjzetJjKRY7crfXGQlKwCPj74MZ0VxX5NY9s/pSw/YvS1ei1WP7raQL9fFcQKo13P7MJ/e/wXfm5+VdbpT+wy0aLGzR7OHnigyQOSiY9MCHIWnVwkqfAo5lTKKeyJ32NyBUVcA5ayO243ntj0BH1saQ168UpfvtojgU8OHDY/tRm6j3TwdatUSaQVpSE2PRadGnSq0liVSCpIUgzqK+3AxMZUqaSuuPKp+PPyyWPHjR0GAVie540GjuccmANAuPYPvnAQT7Z9Envj9yJ0QajJWkchXiFoGajsrot/K96mOn0Sd1DqhMXDcAcc7hMucfOSnf+kHZMw5+AcXMq4ZBDz0Oq1NdLpd1vRDd1XKCcAlmoEt2R1iyyWaEqQVJBkIOMtriimZTusjd0afbEhJ8Zg02VhVfDHU38g/r/x1AAYW1mTG0pcm0Wr1+JC+gWkFaWh7bK2WH9xveJnLUEcd6iJkoTjLG+YQFwyRIImLm9sKfll+SZrxJOVx5ObnrT4mPKgo6UFrsjvbK7lYZm2zKAWPfnOaupeAwQNvFyxdCL5BDKKMwyKgZEcCQCKDXPEE5548lRKQJIvWNbGrjUaW6AuMZ7HO3vewaQdk7AqZhUA49VH9bwe34/6Hu/1U262U6IpMVkG25Y80vIRFFUU4VzqOfrcA00eQIfgDrSBj7zUBA9eEh9YE7NG1TESlaE4UN7Uvymeaf+MRZ8nAV9xzwBAyEa/74y+qZV8pwadEOEbgUGNBgGA0VrZZMtYz6MeWgS2QNeQrigoL0CHHzpgetR0AMB30d9ZZbwjfx2JxdGLkVeWp1hQyxQ/nvnRokYdpZpS7L21F0cTjyLIMwhtgtpIfLmWsvDEQpRqS412+yETC3HVKCHPD5Cv1Ez1MCUlFQDBzfZOn3ew5T9bqLFUmgDcP3OH3zw/xQD/m/+o468lK395tmWwZzBdzStNumRxAkiT1MSBuQGNBqBtUFtwHIdx7cbR5z89/CnOpZ5DhE8ErbJYoatA75W96XdaVFGE6ORoXMy4iKi4KADKks2vhn6Fcm05Ht34qNHf0vNzT7h9Zt5nrhakH/CbPQ1/Qz83P+SV5Rno9I8nHUf/RoK76ptj30g+o+f1kmsktywXT7R5QrV6+iTuIJacLntoGd7q+ZZFnyd2TilhTy3s1uiLt8jEwBBj1GxxMwR/E4x6HvXQvn57o75poggpKC9AmbYM7s7utI470TiLV1UzombgzyuGSUJKcHM5xXLAAFSTx5Gbvl/DfsgpzcHlzMuK1UjNQQwEWSkXVRQhfEE49t/aD6BSSSFOZhMzZO0QemM18WuCvhF9DbpXmZLG9o3oS3X+ni6e+HDAh4jwjaCTu9zNY2wBoHb2JVnFy3dhbk5u8Hfzx/zh89E5xFA5dDb1LP23OClIfGM7cA7Q6rVwcXTB+sfXY93YdfS1eh71kPB2ArQfCb93TmkO1aO7O7lTZZXYHaUUCPVy8aK/tVplCKyFkqtx/23hehT/bRnFGfj90u/UrRKdEi35jPyaqNBV1Lj14MlXTuLca+cUXyPSZPEucevVrVSMYA5SUkVu9H1dffHfHv+tznDNYrdGX+zLI0kp4ucyijPQJqgNXJ1caXr6kTtHJK31yIV0IukE7uTfwZE7R6iRJEEZ8cUw//h8PP774/SxUnBJiafbPV2lv62qkAuAjPXRlo8iNk3QHROVSHUgN8i1rGtILkymux/563LEEtiODTqiff32Bit7UzLagY0G4sfRQqBTz+vx+eHP0WhRI7qdfafPOwCAmf1m4sfRP2Jwk8FV/MuqzugWow1iA893fF7xva5Orsguzcb0qOk4nHDY4HWSPAUIgUSC2AV24PYBXMsW/PBLo5citSgVHw0Q4jMphSn4+/rfdPEg/lzHBh2pTFOM2DAGegRiZr+ZSCpIor8LORchryzPZg14xBBX4pE7RwxeWxMruGbE9+iwX4TALdlNiWsWAcL3IP4dNToNNl3eZLJ9JyC4DydsmWDgZgGAHmE9JMfML8vH3ANzcSLpBOJyhGqx4tLgP5z5ARsuGXaCU+LtXkJ3M7nR1/H3oU5fSaY3tZe00mSxphinU07T7Xf/n/tj0JpB4OZySClMUQxeZRZnol/Dfnip00sGr4mJiotC6IJQi3pf1iQuAAhbfXERNTmJUxMxb9g8eoFfz75Ob4R3+76r+Jk/n/oTfz/9t+JrJMZBDIUxF5FSCWA5Tg5OSC1KxbMdnlXMqgSECVo8Ccw7Oo/KITOLMzHv6DzJeIiaJMw7DBO7TjSq028T1AYdgjtYRadvqrCafPIrKC+gC4KEfMPmH8OaDqPVK8X+cqVevkUVRXjjnzfwzp53sP26IE0+nnQcYzaMwUO/PkTPRziRdEKitiHp/+LGLE4OTriZcxNbrm4x6mZrvbS1wSRvC0j5BSVdO62nb2JHR66HX8b+IggivIIl3wUxpiRD2hh74vZg3fl1mLzTUCEXtiAMLZa0oI+vZV/DnINzMHH7RKM7CBLY1fN6SY0uOSTrXG70iyqKsPDEQpNjri61bvSvZl3FS9teMpsZK75ZyFbppc4vYcHwBfT5VeeEIJbShR0VF6WovOjUoBMOv3iY+gT7NewHPa/HpL+FJCei5z2dchoAcDxR/eQOczr9JdFLMHv/bPp4+dnl9N9KSqGVZ1fiiyNf0B2SHJJpSi7YRn6N4Ofmh34NBWUUWUmaC64Cgt//WOIxbLq0CYUzC2mXq+9Pf09r1gR/Eywpm/HPzX/o96uk3iFyx91xu+E/z5+upuQ4Ozoj2DOY1mSvCfnl+QaGgRhFUtKYcOTOEeo2IZ3LbuXewvxj8wEIkkoSXD6QcIB+Tn5jj28/XrJCP5cmuBDIsUlikVySTCSJHYI7oHVQawxvNlwi+yuuKMamy5twJeuKUaPv7OCsWkP5qkDiOMbcU4BxaTRQOSF0De2KHx/+EeE+4RIlTYvAFnBxdFGUO4shAdnRkaMNXkspTMGNnMoaSmSsZdoyumAa3Fh5N/rpoU/RYH4Do+VevjjyBQCgiX8Tk+OzJrVu9P+z+T/4OeZniZbWHGQbWlBeIFFOEH+a0gRSWF4oaUhB0Oq1CPo6CN5fCBfS39f/xtLopfjhzA8AhK3539f/prpmJXWGOUxdpEocvnMYB24fMPr6rH2z8NWxrySqEHKxi5UNhOTCZJxKOYVpu6cpHq9jcEdM7TVV4ib4buR3eLKNoNYhsRAyCciRS/wySzIxY88MPLjuQey6uYv+Te/ueZeO01i5CCWVFuFUyinkleXhyJ0jeLHTiwj3CZcUIUspTEF6cToeam5YrKuqlGpKaVCRMLTJUIR4hVhUrGvMhjGYsWcGkguSJYodsU6faMoJns6eBqvY+p718VSbShnxiaQT2HF9h+Q9xN3zRvc3EPVsFFaOWSlxAYl3BqTBjLzee2JBIlbHrEbH4I5WSW6rLj+cFu47JVdT++D2iAyINCmBJt/ptqvbMGXHFGh0Gonq5ZkOz6BCV2FW4UUmdkvqFZHcAo7j6O6WqInEPP3H03T3llygXJYkwD0AbYPaGggjZvSeoejGswa1bvSJgTbnr1JaAc49OJemrosvVKXVTOeQzopFlrZc3WKgqRUHkeYenIuH1z9MV4+W6s23/GcLpnSfgkD3QPQI62HRZwhTuk+R+IGN4e3qjWFNh0luAqXuTkuilwAAvj35LbJLsg12K2ti1yAqLopOTvG58VgSvQS38m5JAn5HEw3TwwHgVPIpxefl1SOHNB1itnmF+HeWv1dsvFY9sgqJUxMlmb5pRWk4n37eKvX043LjFP2+lvYFIEE3rV6LhSMqt+Xiz/cMlyasrTi7wmCi2zNhD9oHt6ePe6/sbZCLEegRiKMvHcXQpkNxIukEIhZGSIqvib9TVydXDGs6DBG+yv0aYl6PQeFM2/n2iysqm8fIUdoBB7oHYnK3ylX7l0O/BAB8uP9DLDu9DKdSTim66swFVsnvYMnOfsEJwdvgwDnQCUZJVbbh4gaab2TsOirWFONS5iUDJV1acZqkNIk1qXWjT7InzXXsUaqmSNQlvz32G26/dZtux+Up/2NajkH/n/vTetSkuw4g+JDl7Ly50+A5svIw9mPxs3lcnlzph6yJkqQqDRPIeEhgqW19Q8mgeFKbunsq+qyS7lbyy/ORVZJFL/TiimKcTD6JFWdXSEoRZBRnKGrGe/xk2aTGgTMp3QQqv7c2QW0MVjYkDsBxHNKL0nEz56bkeOSzSju6qpJTmkNrsxOOJR1DSmGKwfHredSjK2jiziLXYGFFocToiGWaSrJcubH74sgXkkSr0S1GS4LBAODIOeKzw5/hyU1PYtHJRQCMVzUtLC/Eq11exc+P/Kz4ekZxhtUbgFQFU6qaiV0nIqM4g3bXikmLQXZpNsJ8wmi8zkCnz/OSOvTv7qmMebX6rpVRZR3xIFzNNh3wFcOBo+MQV9YVr9rf7vk2OjXoZLQsCSm9Lc7K1/N6rDu/TvH91qBWjb6e1yO1KBXv931fklmphNhFQi5oYvCaBzRHoEcgWgS2gIezB3pHCMXKWgQKwRYSfCWzr4ezBzo36Ix+DfvR94hRChqTRBpTdbLFF+xjvz+GpaeWIrs026Dcqzm+O/UdLTVhisLyQkTFReFE0gkEegSiY3BHkzVuACFRBJBOSr9f+h3pxen0+yErbCUFRch8y9rpibOECVezriq63ogbCRB+548HfYxfxv5Ck+xIcIvAgUOD+Q0QuSRSUV2hVkCSGGm568HbxRthPkJV0DZBbXA77zYO3xFUPPll+Zh/fD59r1jKt+B4ZTzq9a6vw9XR1eBv3XBxAy5nXcawpsOorFOe1JVbloudN3biStYV7LwhLFiUkrO+H/U9kguT8dTmpxSLerWq1wrB3wTD90vb1YIn4/5ksOGO1cfVB3lleTQ2tOiEMMHN2jeL7u6+OvaVwfHEhj23NBfPdngWgBCANdbH2sfVB039m5oN+Ir5cfSP1EsgriCw/vH1eL2r0ONhUONBeLrd03SnMWXHFHBzOfp309IaohimNSsBK1HrRn9N7Br8df0vqrM1BjFWgGDk9bye3nw9fuoBbi6HRr6NJD73Ey+fwIbHK6VSxN+WkJ+AixkXEegeqOhGUVrpkIvD2EqVm8uh7TLlbFi1pHBkLCObj0R6kdAq0Fz6N0nwUXKzEONQk4usvmd9DGs6TNHvXaGroL7SRr6VNZD6RPShk6+Pqw/e6PEG3JzcFLsnAbJ6+jDcAaoFqRwqP0+YTxg8nT2xYPgCnEw6iSbfNqF1UlydXCUZoeKdmHjidXRwRLmuHJ4unjj5ijR7OcgjCFEToqD7SIddN3dJFiXN/JtRubFYEaW00nfkHOk1I3YRkXGIYwe2goxbyd1LCi3y4JFTmkOvZaByd0dKjMuPR6jQV0h2XsZEAdkl2YjPjTfY7QFA9CvRSJxquNjo36g//W3Ei5GNFzfSGOHSU0vx3r/v0VLQy04vA1B5L5PaX+L7k/wNnz/wueJYa0qtG31AkGeZajQSuSQS70S9Qx+PbjEas/fPNrj5RkaORGJ+Il3tHEs8hpXnVtLXSXGu0ymnodFrsO3aNsWAjlLdG5Jgs+3aNrN/l1ynbw1jpNPrqNrDxdEF7/WtTKMf0WwE1ecr+aHFAUNysysZdjJOUw1P5MFHOUOaDMHeW3ux4uwKg9eGNxsOF0cXFM0swvU3K7fhkQGR+GaYkEmp1Wsx//h8tF3Wlk5gax4V9NkTu0zEb4/9ZlQZYU2eavuUgcSX/K7E70wI9gxGenE6pkVNw5dHBZ+yTq+DA+eAbqHdJHGgFgGVO0vxdUFaf+p5Pc17IL1zkwqSsP7CekVXRFP/pmZ1+sFewfjsgc8Qmx5Lf/ebOTep/5kHjzDvMIvjVWpCMpqVaj2tPS+sjhPyEhD4VSDKdJUBWnJ/kr+P1tMHLxEgaHQaSeEyY8UZSR6DUvmQ7mHdFYuicXM5jPpNMNriiZ4Ydh9XH9oIRg4Z9397VsaC6Gt3JwC1fp9a/dXFf5gpQ3Mz56ZBhTmlpgR6Xo9r2ddoEGT0+tGSYKLSl5ZUkISRzUdKZlGlwCsJ1ljia6+pTr9nWE9JaQJAyHp1/VTY+pfNKsMXQ76gRuNc2jn6748HGXboCfMJQ4RPBA6/WJk4JF5JkL+X/AZy1YoYcw3bOY4z+ls6OThBp9chrSiNGpw9cXswev1oui1PLkimqyByHCJv83TxxNPtn0aId6WLSfx7dG7QGYMbD1ZsNF5VlLI2yc5jwOoBkkBbcmEyXRESNVGnBp3o9Tao8SB0DREqQ4r7NyhdS8kFyZi5d6bkvSeSTmD8n+PR/+f+BjuoPfF7JA1BiGRQvDN2cnDC7bzb+PPKn5J7zv0zd8TlxMGBc0DzgObYd7syyc5WjG09Fu5O7pJAtByyo1HqOUGqZ24dtxWzB85GE78mijp9grF6NsQIy3f2el4Pj888EDBPWWhhqj+Hv5u/UXeSfIcjXpSRf7+/932jx64JNpvqzRlTcTDm2xPf0ota3M1o3hEhqcfY7K1kjEY0G4Ed43dgUvfK5iPiiwSQuiLUdiGQc8gVB+LEqK+PfY0Xtr1AH5NMRQCKZQD6N+wPTxdP2kQakE6ARP0gDqIak6YqHV+MOFC5+cnNkvr+O2/sRFZJFpovaU6beAxfJ0xuJH6gpNKasWcGfQ83lzMaqHV0cISni6dVMhdv5902mODEsQJxxqU4ZkNcg4fvHIZWr8W+W/vA8zzdgYkzpuXX44f9P5SUmJD/nRczLqKZv2HJXpLjMKTJEIR5C7EF8Q63oLwAK86uQGpRqoERI+N1dnQ26k6rbRw4B5M6feKSVXLNJBUkYW/8XjT1b4o5g+agkV8jiTzyjyt/SN5vrN8xOf+qMaskz+v0OqFOlSjm9tPDP0GOWCxCSMhPoL0+xrQcI3lt69WtaLiwIa0AIC554uLooppcE7Cl0a+CMS3VluLx1sKXI1b9EBeRsdlbSfuu0WvQZlkb+M/zp88RJQZBnGVZnWxP+fHMEZ0cbdAwQ1wZ9L1/38Pa2LUSnyYx2ErB1+ySbFzNuooXtr6AgY0Gop5HPYkstX1we/xvwP8kwfQP+n0AAJJzAMpNvI2V4v3+9PdUaQEIKzSyw7iQYdgsnud5yeS/7vw6SZNusuI/nngcU7pPQa/wXpKYTEJeAhLyEizqJbrv1j5wczmjZSvyyvIMejHL3QRKeDh7SCa6oooi7L+9n64AJ3YVdPrXs6/T3SPBycHJ4D5oVa+VZGFDGgWJIYb8ta6vYc8E4ff5/vT39HWxYWtbvy3a1W9HH9f3rI8STQn+jf8Xx5OOY3DjwbQOkhokFySbzEH5+ODHKNYUKxr9pv5N0alBJ7rgI0olOUN/GYpfYn/BuM3jUKIpofk7YsNJdgnPbX1O8Rjk/OIdk/h5MUpSZnGtMDHk9yW+fzJJT945GYkFicgtzUWfiD60mx0gxIWm955uMku8JtSq0RevNsVfZlFFEdota2dU/63n9Xi166sY0mQIPj38KQDBL0yChMaMfonWsP7L/OPzDfzgEb4Rij478TgrdBUY+etInEo+ha+Pfm3wvp8e/gmTuk1CkEcQuocp1942xts93zZQDXRp0MVA1urr5ouRzUeie2jl8b888qXB8d79V5Cpbbq8CRW6Cmh0Golxfe/f9/Bv/L+SBttEqikP+IprGQHAwuML4fix8spa3gymY4OOJoPEPKS14y9mShP2Mksq5bXfPfQdjr98HP7ulRNqWlEaLmRcQPv67WGOPy4LKz7xpCTmWvY1g+tPfI0aSybU83rJMfW8Hj+M+sHgfUqt7+YcnGMgIvj5kZ8lBkDJ6DTzb4boV6LRsUFHBHkGoYlfE1rOQI6Xi5dk51qqLZVMYPue34dbbwllHXJKc9BmaRtcybxicJzqErkkEoPXGI/JENWKntdDz+vpLoY8x4EzupMXM//4fGy8tBFH7hyh17p4B0iym42VIiffs9xVq/T9k969YsWOsUY0JOhOxB2XJl/Crbdu0fu9QleBY4nHJIFgrV6L6znXVfMy2GylL15dnE45jUuZl+i2Xg7P81gavZQaldWPrMa5187R5Cv5LEvkg8SfJ66zoyT5W3F2hdHkKLL60+l12H1zN3be2EmNasxrlcqBmpRGVaqn39S/Kfo2NJRBAoKxJIk+Sjp9MZczLyO/XFo/v7iiGJklmXTFWFheKDGwYkhVUkCQME6LmmYyHiMZJ8+bTM4Sr/R7hfcyMIxi18S1rGs4kXRC8j2T70yu4FCCTOqmkuDkN654RSf+fRw4BzrRyLuW6Xm9JLdj+RmhZIY4k1iMXOk1I2qGJMu6nkc9g3iPA+eAr499jUc2PII5B+bgVt4tyepePNbkgmTJbxubFiv5TeJy4mgAffu17biSdYUGp61B34Z9Te4kyLXk7OCMLw5/ge4ruuN44nHMPzYfTf2a4lbeLeyNN91ZDgCNc/A8T42ykj/9UuYlxUVit9BuCPMOo6IQ+fjEkBW4+FoU5/8EulfuGKf2mope4b3od+7r5ovGfo0xpbvQI5o8L45Zits9qkGtGn0XRxeEeIVgUrdJklmSqCaISkOuXdbzeombwc/ND15feGH9xfVo5t+M9mUlpRPIzUqUExX6CuqzFscKCOfTzxvd9pMJxd3ZHSHeIbiTf4fWyhbfXJN3Tsb3p79HvB7KiQAAIABJREFUZkmm0dWkMRaeWGhwgZ7PEMYkXqHnleXRujV+bn7oHtrdrK6YVIoU3+jHk47jZs5N6iM1lzVLWHJyidHXSOBSTGpRqsmVvp7XI9AjEAtHLKTuJSU4jkOrpa3Qe2Vvg4YmAPDBPuOfJZDdl7FtuBLi3svi38HZwZn6m+Xp9TzPSzqWEbWY/HtYNEJwVZTryvFG9zfo80cTjyKlKAUf9v8QAe4BBvXhASH/ZNPlTbiefZ36rJWUPivHrMSlzEuSICnR/gOCdLb5kuYI+lqosURyY8RGq6p8euhTSe3+gvICgxIQYvS8Hm5Obvh6+Ne09lBSQRJm7JlB+1MY24UbO94/N/8x+R6lPBpfN1/0b9TfoCGTktGXuwEBqRdj17O7sPjBxQCEhUsj30ZUPj5u8zhwczm81PklpE5PpcpBJfUOoE75cLNGn+O4VRzHZXAcd1H0XADHcXs4jrtx9/8WObEdOAekFqViTewaib6cbNm3Xt2KUk0pNj25SfK5jg06SoJcj258lP6bVKf87cJvmDNoDqKejaKvES14TFoMLYylhLxjkhgi10srSkNKYQoO3TlEV22df1T295sqK2wp6UXpSMhLkEws5MJ4os0TSCpIwqmUU2abryipAwiWSDYJZdoyk+dq5NfI4Dl/N3+qLiH+1VVjVsHfzZ/GSvzc/DChwwSTW1lJLEOs06/CDdElpAv2PbdP4t82x+gWlcW3uoZ2Bc/z+OzQZwj2CoazozMWDF+AIE9pUbpgr2DJ90TcCeLSxm5ObtRl0f/n/pg3bJ7kGL6uvvjkgU+Q/W42ckpzJK6lAY0G0JW/nteD53kMaTIEUROioIQ8kKvjdfRamNBhguQ1svhSUslYyv/2/09SyiM6OVoxLkQQ9/Yl37c8A9nSUhiAZQsYpV15Yn4iNlzcYDDBejh74MqUKzj20jH6mtJ1Jy6otu78Ovx3lyDFnLxjMjZe2kg/Q1b0B24fwOqY1TRxTGL07/4+K8esrNLfbimWrPRXA3hQ9tz7APbyPB8JYO/dx2Yhf1iJpkSS3k+MZGx6LNKL0zFl5xTJ58a0HGP0jz+ZfBKz9s7CM38+gyFrh+Cb45WddIjPvyrF3QgkOEgy6Yjv9VbuLWy4KK2VPb79eMlja8zO+2/vR7muHDzPw9PZU5Bs3j3uwEYDcSFdCIwqZROLs2Nf3f4qAOWb4Xz6eUTFRZlcjZOCYdHJ0ZLqnnKUms+MjBwJXzdf6D/SY91j69Dxh44YGTkSy0Ytw6z+s+Ds6IxybTkWn1yMRzY8Qj+X8HYCPJw98ErnV7D96e2Kjd+rSlRcFEavH200me35js8b7ALERtHPzQ/pxen4cP+HuJN/B0kFSZgWNQ3/xv8r+Yy8dgspxEXKNQDCBPrev5V5F/ll+Whfvz1dTFzNuorlZ5YrKk2CPIIku2QePPzc/CSB+nCfcCx9SHCHyo1+ubYcTg5OiAyINDh+gHsAJnWbZBDMrCpKwWdj9AzviRJNCX46+xM1/vIet0oJU8bQ6rWSXafSzk7peGSnL99xH0o4hCZ+TdBnVR+ELRCCsEqLJIna8OS3AATPBaneKf/MvKPzMHPvTDzX8Tk6bgKVc3I1V6UpYdbo8zx/CIC8PdMjAIhucA2AR2EB4hlW/CWI602fSTkjSXSgAzWSqJBWlCYpX0vaxwHK/ntAMOirH1ltcqxkRlZaDcsr8SkF6apC5wadaU0iOY4Ojij6oAjv93ufjkWs2FkwYoHBZ1oGtsTwZsNxYVKlYoZcVOIJ6T+b/4MR60aYbbl4Leua0SqBcoiLDRBu3lJNKWLTY3E9+zrOp5/HhfQLePqPp6k2/XbebXx8SJpr4OTgBAfOAd6u3hjdYrTk+xaPv3dEb4xtNdai1XtSQRJKNCWKtZeAu3EV2WQtNgClmlLJNag02QKVQXQCWSyYKsIXuiAUzQKa0fceuXMEr/39Gnr+1NMg8PjHlT8kcl6e57Hr5i5JMqMD54CEvAT8duE3yWQKCBNOkGcQBjceLKnXTmrW7I7bbVRbXh3ExdGUeKLNEwjxCkF0cjSCPIIQ7hOOCl0FVSUBpnsdeLl4UantM+2fQYfgDpIFotLEqWQXlAz52dSzeGDtA7SdJCnJIp+UjOHk4ERtHi0lc/dvIa4scl8puXde2PaC2dpV1aG6Pv1gnudJamsaAKP1SDmOm8hx3GmO407n5lZqXY2V1CWJOmJm759tVKGRXZpt1HWj9IWdnXgWz3Z4VtLSTo54e0nG+cZOwe/6Zo83qyzJNIfctSE3sPOOzMOY9ZU6342XNtLPiHt/lmpKkZCXgB5hPXA58zLCvCsLU5EaPUqNUXqG9zTayPnXC7+i1dJWFhU12/qfrdj17C76eNa+WbiWfQ2df+xMjTzR6d/IuYEybZmiWydsQRgaeDVAWlEauLmcRJ8tD8JZuv0lTcWvZCkrU86mnpV0RyrTlmHcH5W9a5MKkqplDMkCxlQjDUCakSrudiV3HwGVSpSn2z0NLxcvFGuKJXLQ3NJcg5o0RH5K9ODOjtJ6+jx4lGhKEJ8bbxX3JMHH1UeyM5FDKmnqeT148EgqSEJhRaHku5aXHRZTVFFEv+PXur6Gpv5NJQogsRCBoNRHgNigv8ZVNk2S7wpJ5d2MdwzdnCSuKGZP/B4qMvjsAUO7BgCD1gwCUOkt0Og0iFxSGQOxiU/fHLwwKqMj43l+Oc/z3Xie7+bnX1kczFgNFTIDitHqtRZ3lxez+Yq0GXSgeyA6h3TGw+sfxuLoxUY/Jw7qkn6kRDnUt2Ffs+4ipRvVFDFpMbTuNiBolwkanQbv730f269vVywVLdb374nfg8bfNsaJ5BNIKkjC6PWj0a5+O0kDdbkrx8/ND0kFSTQPQg7Rm8vL+yox+8BsbLq0CTN6Cyqs1TGrqVRSCeKTViLQPZAa9ONJx2mRts+PVGZS38i+gWtZ1/Bql1fNjo2cx9iOMbM4k27FARj0IqjQVSjWaApwD7ColLZYR2+KoU2HYma/mfSxksadTAovdnoR0a9G438D/idZOMkN3cVJFxEZEIkgjyA0C2iGW7m3sPTUUuSU5iDIIwg9wwQlGKlYaWmrPzlKq+X5x+ebVLa9+c+bSClMkQSsSzWlGLtxLAAh2CxvY6nEk22exDt73pEE3401AZp3dJ5Bf2sydrE7SL7AMBXTMjYxpRenY3DjwXi6vVDSo1W9VpLXNToNHmz+IE2ilE+4asg2q2v00zmOCwGAu/83HU0kiMavVBXQGHpej5GRIyUBpm+GfWPW1yvvVjOu3Tjql7MUcgwSFI7PjVfU5E7rNQ0Tu0xEA68GVJtrKe/0eUfikyXlXX1dfSW+eH93f4xuMVqSMCau3EhcUiSQdizxGDKKM5Bbmku12fIqo3lleYhYGIHHfn+sSmNWIjY9Fi9sewGPtKp0KSiVZibwPG/0oo5Nj6W+Vw4cXSk90LjyN08vTselzEsWNTmRb6/lpBalSvzz8uqlSQVJip+t71lfoo4J8w7D+30rQ1xbrm4BYKhIM8bMfjPRLMAwC1dMt5BuiHkthk5SxPdL/kbx/RTqHQp/d3/8HPMzMksyEZ8bLzFmmSWZOPHKCavUeSGT0aeDP6XPmevORcaq5/VULjlhS2Us5VjiMTy/VblXsZhNlzfhZPJJvLL9FfqcPFObqAPbBrVVVAgCQvY7wVi1XNJlT4yx3IYWgS0Q6h1Ke1qffe0sMmZk0CQtjV6DvfF76UJSHnuzp5X+XwDIL/E8APNVySCdtcTuGnPqER485h6YS7e1D0U+hOl9plNDJjaYAOiugKwcvh8lrLI2X96s2OzAFN+e/BbnUs9Rn7tSQSbAUBHAzeUkWb+mkNfTJxfAiOYjDL4bYnj6N+wPbxdviT9baSVy+M5hpBal0mOa2mpbi/4/V/r1TQUFeVTq9OVp52XaMonbjpQuFv/W5LNKZYPlkO9JLskTI57M5e5EHrykbSJR0MgL0iUXJivq3I2dV57S/9aut7D/9n7Jc3KhAMdxWHRyEUb9Ngpv73qbFm9TCsgXlBdIEvhWx6w2MCzXs69bxbg4cA5Y8+gaPNyyMj4VGRApKaUth1zf4jwGeUzPXNcrMWI3mXzBQb5XjV5jcB8MazYMD7d4GL9e+JU+Rwo2EohAQl4XTH4uT2dP6vefN3QePJw9MPJXoUyDm5MbgjyD8PkQYceq1Wuh0WtoBzFxiWXARit9juPWAzgOoCXHcUkcx70M4EsAwziOuwFg6N3HZvFw9kDzgOaY1msaZu2bRd0oreq1Qph3mCTTVIye1yM6pXI1NTpyNLi5HA7fOYxhTYdRTSxB/MMBwta9Y3BHo6sZc1r3Ek0JXXXqeT12P7vb4D3fnfoOy88uR1pRGl0xGmvYIOerY19Jsg5zSoW4eXxuvORmzCzOxPbr23Eu7Ry8Xb3RPri9xAia2kaTG91YALKmGNuCm9LQ8zyPUO9Q/PTwTzjyUmVwWn6zAcDLfwmlCX6/bJi0MufgHLPjI6s8pWMrIXflvPnPm5LVIbmWjMUIxPx+6XeD+NKO8YLCRavXSlbGFzMuIq0oDWsfXUsnTHlLzJs5N7E6ZjUSCxKx48YOmnxFfmNiKMJ9wlFUUUQ7qQHCZCqfHFp+1xJavZa6AOu5mw7sG8PJwQlpRWkGWdziwOf17Os4mVS5cNLzejTwaoDFIxdbbODELVOrw/Xs67T65d74vajQVcDH1QcdgztKdnPv9pUG5cnvoef1Rt2hABD9ajR2jhd2LVq9FhwqixIO/2U4uLkcBjcejMKZhXSXSl6XZx/Lm8RYA0vUO0/zPB/C87wzz/PhPM+v5Hk+m+f5ITzPR/I8P5Tnebm6R/lknANu5tzEghML8PWxrzFwtaCxd3VyRYRvBE6lnFJsChIVFyXxbb6zp1Kp0Cu8lyTj8J9nKhMzzk4UgrVR8VGITY81umIglfqM4erkSr/8k0knDXYWcmqSnSvmQvoFyY1AxvBEmydwK/cWjiUek0jFjJWjAIDuK4QJVQ01AGA605UwuPFgvNXzLfQO740BjQbAgXOAv7s/Hm31KJ3oAMPKheLJWpwxOWD1AFgKka72bdgX3FwOcw7MMfl+pYC3+Leo0FVg8YOLLaqPciH9gqQ88/GXj9Nd2es7XsesAbMk7/dy8cKEjhMQ918hgC2eWMa1Gycp5c3zPMa3Hw9+Nm9wXSpJ/sq0ZXRyEMtIefBUwGAuy9sYGp0G7/37Hn46V7l7uZFzQ1LaoOV3LdFrZWW7T7FOn+TcmMNatYLOp5/H0F+G4qujX+Fq1lUsO70MPHg6KZLJitRC+uX8L9h9czdKtaUGhfBIvR8AWBOzhgoWntz0JJafXU6vHRIbXHVuFRafXIxJ3QRXEVncieNKkQGR6L2yt9VdPLWakSs3hmSWSytKo/4z4usSE5seKwlwiI3CJ4c+wax9lTeN+GYmWz2lomTmGNFsBP13XlkevUkzijMw76g0mUYeZLbWj0Rm/QZeDbDsoWU007O4opiqUcSJZSTrz5QBVpqQ5g+fr/BOAaIjNgepCW+Mmf1mokRTgkUPLsILnV7ApG6T4OniiRJNCZaeWophv0jdJP0b9ke30G448PwB2sykJpA0fbJal8dlXu/6uiTwp2SAxNfm1ayrmLVvlsnEI0LroNZ4uUtlEbUl0UskbSkT8hIkVU7PpZ7D/GPzFTt2uTm5SRRLcblxtBk9oYlfE6x/fL2kcCChTFsGH1cfdAnpIi1pwfPwcfXBu33etaiWkRJk4lbKbif3xMudX5bUlBrWbBhSClPw9dGvjbp55ZOZvJ+spcj7ZpB7OtwnHKdTTtPxk/vulb+E+ID4uiBBbrlqTLxoIXGBFzq9QPM05H/b/OPzMWvfLDzY/EEEewbT18V/642cGyjRlJhtlFRVatXoy1eiT7QWZE7igKu5NormEPvc5fpvQt+IvrSUghLODs4SVUx6UTpdUfPgDeSLcndSVWkT1EayXZw7aC79t5eLF1Knp2JS90n0Yvzn5j/0Jlo5prKk7qBGgzC522Q81sp4UFZppW+q3WBV2se5OroaVVldz76OlMIUJBck47W/X6PumhvZNzD7wGyD9/u4+kDP6zGw8UCjaiiyklVqgWmMxIJEODs4Gywu5PWPlAyQ2KefUpgi2SH+MtbQz0vQ6DQSEYI8r6Pxt41pWQdAmJBm7JmBzj92liTaFVYUYnXMatoOlJBTmoNX/3oVR+8chU6vA8dxiuUqAGHSbx7QHM+0f8Zgoo5Ji8Hq2NXV9iMrXVtzBs4BUPl9ert4S763p9o+hVb1WuF06ml0DO4oMXpkByC3G8YWAfKic/LyD/KYF1loaPVaye9NJlsyaYpjNEQkYWq3T74/J85Qp0/Pcfc7iEmLQX55Pv2MUh6JXJBSU2zaOmdalCCLE6+MTZVLqCn1POrhypQreLHTiyZ923LFgfgxuYgtwdIkDp1eJwmuyWMP847MQ99VfSXb9Tf+EfIGyEoCAN7v9z4md59skMYuxthqanpvZcNPgoSW8OPoH40avz+u/IHEgkSELxTqqJRoSpBflm/UwJTryuHl4mWynr6lNYPExKTFQKPX4FzaOeSX5eO5Lc8hpzQHUXFRkqCsknvH1IpLrDiRs/b8Wnyw13R9IHFSISEuN04yGRDIPSKOMfx07if0+7kf5hyYg8ziTIMGHCReNrXXVAAwkP/y4PH/9q47PIqq/Z67m00hhSQQUgiEUCREIPQOkQ6CNEWlFxHwJyhFFEVF1E8/EBUVEQUR+FBBpAmK9E4oBghICZ2EGAghFdKz8/tjcu/eOzNbgE0AmfM8Puru7Gb2zsx733Le8+YW5iLldopqUpij4I3+7iu7QWYQRkWm13nOwTm4VXALZsmMpXFLsS9hH3IKc+RhJaZygoG3dq/y9GYejYJkOfLHAx7HzE4zBXbfnhF7rN5rr/75KjOsR8ccZfM1bKVLeVotoB0RLzy6EEnZSWgS0gTf9dTuZu+7oi8qeFRg10Urh29rUMvdoEyNvrVFd0T7xZpK4Z3A190XERUjMGr9KOHGsSfoxIfiTSs3tdtoE+IdAmm6hMJ3HBtSEX8zXmAd8OyA2wW3MXXbVOxP3C8wQGhahx8ScTH9Iup+U9fqQ3E547LgNfOSu8pRgbYws9NMzdfH/j4W8w7Pw+utXtd8XwleB0aJInMR88YPJR3CrE6zNI8DwPoCHAE1TH+e/xNL4pbgf8f/hxk7Z+DarWsC11rrQeNDeIpQn1C7OeaTKSeZt2ivBhDiHSLkdZVzFgDLZkcjP35jiE2OVfH0L75yEUaDEZ2qd0JUUBRiEmOYNgwgpz2MxMii2SVxSzD30FybOvha4I0+jSJik2MBAKPXjxaudU5hDoatHYY2P7RBQmYCzJLZ4aLlqRunNF+nsuvtwtrhja1vCNRmAzFYjVpzCnNYU6i/hz8IIcjOz8b5tPOaQ2wA2RHcP9LioKbnplttamsZ2hJPR8qRPO2J4NGpeifUCZD7BrTSr84elF62nr6VqNGRcLJz9c6MdhfoGYi53edaHebhZnRjee2JLSayB21wvcHCkA4K2q3oSDHySPIRzS6/oVFDMarhKIR4h9idNqXEtLbTNGcNmAwm4SbQkkuYe2guAPmmi5wn10is5Zk3ntso9BDwImmjN4x2+Hx53RgeeUV5GLdxHCa3sp4u4iFJktUN/2TKSaaWSkAwpbWleK/8zJ2kd+gDFOIdwqKk7IJs3C68LegHdQoXUwh+7n6atZrqftXtasPwxfZ32r1j40hZK1+rt4Hf2LrV6IYzL59hkSSfauBTUIC8NkFeQThw9QC2XtyKA1cPILcoVzhmZf+Vqs+N3zjepg6+FugmtPzp5cgtFP/GD8d+wNmbZ+Hv4Y+w8mGq9IhZMmP1GbV+ky1Y68+gkSHf1WsgBkxtY10ijLK63tz2Js7dPIfPD3yO67evCxPTePwv7n8Cw6vfL/2w7ow2c93dxZ0p7+4ZsQe503KFKW1L4pYwRhOTYHazpLn5+QrOwH3x9JW0OfowrXlujdXPbrtkaWBoEtIE59POo8F8bZpg9pvZLBXiYnDBr8/KnbnWOPpUQM2RAtHaM2s1ue5myYwCcwHrHSAzCMgMgnmH5+GTfZ/YHExBN6WMvAx8F/sdu2n71ukrGDiqwc2DMi0caZ2nxSd6w92pBLSjCJxtVZVDwImUE6oUzdWJV1HDrwZu5NwQitSHkg6xaWJKLrOykAnIjJktFyyb35mX5YY3+vfGNR2H7rVk7jQ1/gJPP1AsZo5uPFq4FrTQ3zG8oyM/lYEW2+lGpdSmof0IPF5p9oownQuQi8HUs6ZppzfbvIlPu3wqbE5nb57FzH0zmYf54e4PhXGbgNyEpLWhWRuhqcSvp37F7YLbCPUJxepnV8sMKQ15DAMxIC03DSajSZX6DPIMcpgAQaWfJ7WYpPk+dXr4LmhvV2+bzLUnqj2BJX2W4KcTPyExK9Fuajbuepzq3qWbtZEYhRTr8r+XM56+0WCEu4s7lvZdKnyWDlOn9xh1LIdGDb0nqWstlKnR93HzwY5hOxhHmaJJSBOkvJZi0wjxnPfj149jzsE5woBoCgMxoFgqxrc9v8Xa59Zi3uF5iEmMQdzYOCZjqgStzjsScUiQkDhRLdi07PgyLI1bihs5NwRa4ct/vIzXt74uzLhV4sM9H8IsmfHh7g8xZsMY9rpSWpnP33/X8ztB10TpvWnh+PXjWHN6jaagnaP4c5DawALWJxLZQvsl7VHLvxZWPLMCcWPjcHzscVT2qSykNyiiF0czvSRlrlWrGar+/PqMNgdY6iTU0zcQA4zEiK41uqoayAqLCzHngDiab+a+mZrSIVpG2ha+OPgFUqekYv0AOQU37695VrWcaHpiw7kNwgzcWftnCbUWmrr4v6b/J3DYqdrkjF0zcGDUATSv3Bw3cm4w5Vha6Hx96+vIKcxhaU4/dz9U8KiABoHW5Q/O3jyLfQn7cDLlJPqvlCUQfNx8cCXzCr4+JNaBaP2AUqbPp53HtVvXcHDUQUQFRiGiYgS+6fmNw13BHavLG62WThePaW1lVl+H8A54vNLjeDf6XSzru0zz2JWnVrLit1kyC1IoWiAgKkosPf8Lr1xg1xeQU8I5hTlYeXIlGn/XGGQGQbBXMN5u+zY7hhp7PpXj5+4HAsIE35yFMh+XWMWniqDuB8ghaYBngOp1a7BltH7q9xOmbp2KvnX6ondEbxgNRiZPoDQm0nQJ45uNx42cGw5riF9Mv2hX8rTHTz1Ur2kZMiWUPQoHkw4K3g8Vb2pbtS1irsYgKz+LeRff/vWt3e+Pvxl/z0UhmtdWpsIcKVq3CG2B7jW7s6IVIAuh9Y3oiwtpF+BqdMX6+PWqGaQDVw8UDH1iVuIdD/V+d+e7eOqxpxjDY+q2qZi8eTJSc1JV6bhP9n+iqou4Gl2Fa3Hu5jl80+MbmzITWki+lQyT0SSIgvFDtwGZhy5Nl3BkjLzJXUy/iHXx6+Dr7ovxzcYLdYFQn1AMixqGM+POoOdPPbHuzDoQQuDu4q5K2bgaXYVohc7vBWSjQzeJar7VcDP3pqYOFkXtubXR5oc2bKBI/cD6SLmdgre2vYUfT/wo3A+UCMFfszOpZxAZEIlwv3BmLB2ddUBTH9ZGFFL0ieiD7jW7CyyeqKAoq8dTfaliczFLVVUsV1GYq0AR7B2suufp71h0dBHC5lhSpzRtOGXLFCbHMH3ndJXKKQBhWt6HHdRRmTNQpkb/duFtvPT7S1gXL+e+aGHlcsZlTNs2zdZHHcYHuz/Akrgl2JuwFwNWDUBWfhYSsxLx4/EfhYeAskzohYu5GgMCYlcjJS03Dc/9Kg7iticG52JwsSrpy0NrYzAQA2r518Kyvssw97Ccv9+TsIeNhDt78yxuFdxiWvf96lina26/tF0zFUJTFeObjVe9921PcTMZuFpuBFIWNbWiLh7RYdGo6V8Tfwz6Q+BpRy+Oxlvb3kK/X/qh9/LeWHFyBWr41cDQqKEI8grC3hHqHov18es1awFzD80FmSHTFZ+JfEZQID194zQMxCDINO9N2IvY5FjkFeVhUotJLO2oRcfzNHkKG92ljEt4ZeMrLOXoiOgbRfsl7TFotXjPUIPhafLE5YzLeG/ne2rxLUmSJTu4iONq1lX8cOwHHLx6EHHX47A4bjFq+tfExkEbVdoxJqNYI+KbpkI+C8Hzq57HB+0/QItQuXlKq3BNQTceGmkWFBewekFiViKmtpmqiog9XS1p3Zs5N+H9sTfWnlmLUzdO4fUtrztcsDyYZL9BEpBTaZNbTkbM1RjEp8pDbByJSPl7KzUnVSURAsi/X5nCotfQGlV8ZEPL2NYVJ1cIDpiWHIVyroizUKZG/1bBLaHI+N9O/8WSY0uw49IOQT1RCZ5lYg8nb5xEVn4W+q7oKww7MRqMzMDXqViHpXr43VqC5NAQZuUAcHs8/SJzEWv71kJN/5oYWG8gu+nfaG0plL69/W0cf+k4BtUfZDXnOWnTJMY8eaP1GxjTeIzVQdla57rpwiYMbzBckxr55UFtNVItKVkfNx/Bi+dxOeMycgtzcTXrKtOcp99BB9/E34yHq9EVBcUFMBADXI2uKlVCQE718b+DsmdoCiQtNw3F5mJBcCs9Lx3r4tcJzA9aiJu1b5bA09dibBBCUHWOOJCDp/JqbZg8+N4LLVnvIK8gTGk1BV1ryhvwjF0zUOfrOoKXmZmfaVUdljoi1BjzapMUJoNJcD7487hVcAubL2zGR3s+gqerJ2r517JJSHg68mnUqViHyYBvvrBZKN56uXoh1CcUszvL19bV6Cqksfh0ZIvQFoi7HodetXsJBU5rEXX/yP528/8eLh4wS2ak56Xj+PXjbLMrMhchaZLt2RBpuWlCKnXlqZWaxyhVPBsENRDOS6m+aoulSN/jZcRLC2Vq9JUjDTgyAAAgAElEQVQ7+ct/vIzh64Zjd4LtgiLfWbio1yKMajjKxtHyAir/lovBBcFewbg68SoG1huIPsvluS+OFqvuFbYop7mFucjIy2DFQ56+OT92Pj7Y9QFqz61tNecZ4h0Cb1dv1KtUD8XmYoT7hqsGPNvD7M6zsea5NUycjoJ2/irxWRf18JaJLSZqDnUB5Gu46vQqVPnc8lBrFZ9dja5IzEpEWm4agr2CUfETNWOpyFykmXOl19xoMAoPLWDxWvdcseTgaYNMZn4mlsYtxdD6Mtdaa+O3Z2Tqz7feGwHYl1b+J/sfnE87L7A2EjIT7KYSg72C8X2v79kGRAhBcnYyRv42UnXsrM6zVJGbErlFucjMy8S5tHM2DVB2fjbS89LZWiXfSmaG3EiM2HR+E8gMwijFBcUFwibJp+tSc1Jhlszw9/AXUrfW+jDOp5236Zy92OhF5EzLgYfJA2M3jJXPqcQBiE2Otaq0G1Y+DB3CO2DwmsHM2bAmzzw0aqighhrzQgxaVWklRGFRgXIqiVJqt1/arlng/r7X90xFViultrTPUtVr94IyNfrWdro7YZFsvbQVRZJ9/Rjl33IxuMBoMMLfwx/v7HiHpZj6RPRBlxpdEFExApdevYT6gfXx9ZOONyQ5itjRsVbfS8pOwh/n/mCGip+nCsga8mdvntVs1AHksDK/OB9hvmFotaiVzbGG/h7+GFRvEA6OOijME75deBtuLm6MvmoNO4bJSoVaOXxa/LTn9VJobUx8uiDcL1z1PiAbfZ4u90H7DwBYjETU/Chk5Wdp9lPwND6aYzZLZtzIuYFgbzntpJXWeKX5K6rXKGz1eVB1Vpr7t5U+XHNmDaa0miK8Ru9TLUQGRCLIK0jgnxuIAVn5WcL6/DNJZq3VD6zvEJ14wZEFAGAzOt2bsBfXbl1jm9KIBiNYJ2s132rsmabKuIB8XVY8I9fXeAbW+bTzMEtmoc5hTXwRsNS2ePAjEsc0tpAhbubKEQ91mGzVgnIKc1h/AR2uTiMvJQrNhYIjcCXjCtLz0oXXqvtVx5YhW9hglvbV2jN1VorlTy/HyIYj2QaileIautYxKRRHUbaUTSuDLByhao1sMBJBXkEwEiPLy41tPJa9z1PnXAwuzAB83vVz9lpBcQHKfVRO9bfzi/Lh7+GPar7VEDc2DmObjMWdolftXhjRYIQQnvKwJWlMu3ztsWqseR0f7P4A+UX5jA6o7Dbmw+rosGgs67cMzSo3E/KiYXPCIEmS5kxRHtTAaHkslGZGWRN3A7pOx68fx+9nLSyvsPJhjOZYZC4S7iHKvuEpeT///bMgxEeZMPwxlAZJDfGio4uEY3m8G219iIytNnll6/97T7xn9VjAkvdWbr5aypLn087DaDAKaSxPk6fgbTar3IxtZvsS9mHNaW1adKhPKKZHy3IYjkwjG9VolDDvIax8GGv4Ovl/2tEhAPSu3RtxY+NU9GmzZMZPf1vkKd5u97byowxa0ScfNWoNNGFG34a+P3+/UGw4u0Hz2B+P/yjMUXh+1fP4/ezvwtq7GFzQqXon1nT2RLUn8PvA3yFNl5idWHFyBZYdX4b/xf0P129dv6su8ztFmfP0/T38ET8uXvU6AJvDRw4kHcC1W9dgIAb2AMyPnc/ep1x8ADg+9jjz9D1cPHDghQMYFjVMcxf9eO/H2HVll9BFejcDJcySGRl5GcjMz2Rt3Dxe2WjdU6QPWfOF6m49HpNaavOSi8xFiBsbh8W9F2tO1+LZIXSoh+9/fVUqlVoPu9LzpMcoO0uXP70c58afAwDVVCIleEEvvkhWzbcapkdPRzXfaiptm/eeeA8znpiBWZ1mYUyTMWx0HQCsPCnnXPlISMlS+qW/LMlMH6pAz0CWeqBGgqYRtai7WjlyR6Ccv0AjpO41u2tujtTTVDaHaW3GBcUFMBIju19fbf4qZneZLRxzKOkQpu+Qjfm3sd9i4ibtmsvVrKuM70+dsGcffxaAnG6s+nlVFBQXYNnxZQj5NAR5RXnIys9izxTtvAVkVVr+XuIL4F8f/hrbLm5T1fBq+tUUnk9bbDA68pGPKHklUq1mOVqkv1NZZmvy6OfSzqmel0JzIdtU+0b0xZD6sjQHpRTz99XO4TsByM/juD/GYejaoQj6NMghdYJ7RZka/So+VXDp1UuqhWwZ2hK503KFodoNgxoK+ha0ALf69GpV2P54wONIzUnFCw1fQKBnIAghWD9gPXYM24EZu2bg29hvUdmnsuaNRF/rXKOz6r07wYazG7DmzBpk5WdpDjNRDsagGLN+jKbgmBbC/cLxTOQziAyIxPoB65lnX8GjAtxc3OBh8lDR9LTg+ZEn88qVD4EySqAPGAXVzedv+I7hHdGvTj/2XUoKohL8Z3OLcvHHwD9wdtxZxLwQA283b03e+oh1I3Du5jlMaT0FDYIaCAygOQfltNKqZ1cxQ8Vj3B/j8PMJmalCDQvtko0KjGK5VwqtEY+VZosb+bvt3lV9zhF8cfAL5E3Lw/oB69E3oq/qfToQJsAzQBBb09rMAXnDKmcqh2CvYIxsOBIVy1VkRpsW8ymbxGQwCQwaWmSloOmcX07KGySNqOcemovErEQkZibiZMpJJN9Kxrex30KCxKRBNpzdwDbwEetGCE4Bb/Qnb57MNLcAuWjevHJzLOi1QOUlW0OzELlAys8J4METLZpXbo4uNbqgso+cx69aviqypqrHXgJiF6w9eJg8VLIOh5MOM7rxot6LmPNHI/mZ+2ayps2U2ylMj4f/rfzG99zjIkvQWShTo08IQXpuOoavHS68/nnXz+Hu4i7wVv/T4T94rdVrguY3IBffKHWRIr84Hz+f+BnfH/0ek1tOxqKji9AhvAOeqPYEjAYjfjj2A07dOAWT0YT3nxALgHTBz6SeEV5XTkTicWyMOqdoD2m5aWixsAV+PvGzIJmrNRRE6/wAICYxBgHlAmAkRjz181MspO1Vuxembp2KtWfWWjUOPPhQWMnYUYbdfFrKz92PeZW+7r4Y3Ujmeb/R+g27KQFe1lZpLONvxqNWhVoI8grCnit7rPLD3V3csevyLhy7dkzgmFNU862GE9dPqF7/+vDXmLR5EoZGDcWQKFEY7XTqadU95kikdyLlBN5qK4qoKSdcaeFq1lWk56Vjf+J+VClvSQUGegZiUL1BjEfeP7I/tg61jG+k9wGf0gSA1c+tRpcaXfBRx48QNT8KS44tgdFgREC5AOYUUANsMpqElN7kVpMFbjzNqdOCLBWde62VLAFx/fZ1+HmIG3L9wPoI9AxE4+DGTCZk0/lNwhpaE8wD5E0r7nocuvyvCzO6/SP7CwVsZf+KvZkQfJH4sQqPqYr63m7aGjxa8ioVPCogOiwaJoMJz0Q+wwrhrUJbCdEmIGce6AbnN9OPsaOGNRgmq+VmW+Z5DF49mDkr/DM+9nf5+vq4+Vgdpn6vKFOjn56bjombJgo5ueiwaKTlpgnpj9GNRmNvwl5UmFVB05viawB1K9VFj1o92I36/u73sfnCZqz4ewWiF0ezfOuOS7Kn/U70O9g+dDsrKNEFV3rbtjTcOywVZ/MqizPWcDDpIAauHigIS/GURC3ucdzYODQKboTNgzdjT8IefPPXNziRIhq2InMRvjr0FfZc2YMlfe68mcNADMxLUxo8DxcPfNzxY7zT7h1U9qnMNgVXoysmtpyIGn410GVZF0bds4Zrr13Dj/1kmuXwBsOF9yZumojMPPmBo+mQkQ1GopypHH7q9xMzJuVM5TBq/Sh8sv8TzclFn8V8xsL8NlXbsGlZNDVCQFT034LiArgYXJj+0W/xv7EUmC2sObNG4LwHeQWx32cPtb6qhXaL2wkF42cffxZtq7Zlm2x+cb5mqG9tQM9Lv8vDODZf3IzHKjyGlCkpLJKgG7LJYBIK2e2XtBfkQVyNrvi0y6cs/ZaQmYCpW6cymufg1YM1dZ1MRhOKzEWsoJ18KxmZ+ZmY232u6lgl9iTsQV5RHrZc3MIijf90+A86hHdgxlqZGVDOvlUyzviGuSdrPYnNFzarmuhoKlK54StxM/cmcotyUWguRHXf6sz7JoRopov5pj5+joeRGIUU1IX0Cyx7QW1Qn4g+bMPqXrM7Wn7vWMPonaJMjX5mfqbqgdp1ZRcWHFkghGqnU08zHnbV8lVZbgyQ+c689zml1RTM6TaHsQFuFdxC/M14PL/qeYEVxN8o7cPbszQAzfUpc9S2ZFWV7A4tWVxbSMtNw/Vb1+H5kafQQEP/Js8eenfHu1j97Gp0rtHZar7vwNUDyC/Kh5uLG3pH9MZLTV7CiAYjNI/VAh1ZB8gMA+oh0vemtpmK+oH18XfK35ZBE0X5OHbtGBOkcmQiF13rInMRYzRQUJ0dem2LpCK4GFwwoN4AVnD1dPWEkRhRbC4WxNGoGufs/ZZ0Bc/TpxvZkrgl+OLgF6rzevXPV9lIu97Le+Nc2jm7vwUQo6INAzaoRMZ48BsCNbx8VPJl9y8xpskYlh7Ym7AX9b+pz+SBaY1i0bFFwve+te0txF2LY/cOfw7UADEBP6NJiPJ2Xt4pFA7zivIwefNk5lRcTL+ImftmMvbIpYxLbBRow6CGaFu1LVafXo2rWVcRmxyL/S/sZ1pCXxz8QuCptwxtiT4RfayuDyCnm06/fBq1KtSyGTnSngf6zI5pPEbQp+Gdwsy8TFzKuKQii9DI05GUDi3Yuhpd4TtTjjqOXjuqmYLiGUi8A2UgBqvPiIvBBT1q9RBE9VacXMEKy/YaRu8U91VPn0I5aHhPwh5WVJu6darwvslgQo9aFpkDWrzjjbqWwVaKdFFQCpuSGUMfzC+6WYwE3+F5LygyF2HH5R0qnjplp3wWY+G6rzq9ClO3TUWFWRWs3jSjG49GsVQMN6Mb9ibsRSXPSqxj1xqUjSO0pd3NxQ27hsth/apnV6FtmFxnodOQ6IN2u/A2BqyyjO2zN3A9pzAHfVbID/30ndPRa3kv4X16/ej3JGQmINw3HGQGYQaeDpwuMhcxz5YHvz7NKzdnXixf2NMaGHMj54bNuQFaDWKA2OPRZEETqzMaVvZfyaae8dCq/YxvNh5VfKqgQVADXEi/wO5LSv1U4ui1o4yWCMjX70rGFfRZ3kfFQprUchKLcB2BraLizdybSMhMwNO/yBEXfV54Q9dsoeUeS7mdIvSfaKFRcCO21pczLluVWaBRPa0BHEk+AheDi2ZajqZLlO9Rg00lWgA5TaoEn2mg0s2ArHSqpXrLQ2n0tTCo3iBsHrIZA+sNRJsftOnSX3bXbsi7W5Sp0ecZELRoW7FcRU0jTbsslcbkQNIB5gVMj57OqG32Bl5ba+ag3ZdUz5piZqeZyH87Xwj/KEf9bsDv1kXmIs3dmxbulHKuy/9ejrTcNKtpB8o9dnNxw9A1QzFj1wzN43gcHHXQamcipanxzBoq0kVvXmVkxDOWtGQp+Ju+Zy21lgl9n17v7PxslRBa27C2MBqMsg4/56HSNAb/WnpeOpKykxyiAxebi20Wn3vX7q35ehWfKkJUlF+crxqzuHfEXsQkxrANgZcB0PJmm4Q0QcLEBOa5UqOklUpoFNxI7j/h8t81/GogIy8D6+LXsWL3Rx1kpkyoT6jD+jbWQCU7PE2eQtPkzE4z8eSPT2JtvLZhtyZRzP+WY9eOMfaLLQkI5bOTlZ+F+HHxLLX5fN3nVZ+xZvQBYELzCXA1ugq9DTR9aG29Cs2FmvRK/ng+5z+/53xVPQaQna9Qn1CVLAcPXoTRGbgvnn55t/KMZWIymDSNfsvQlpCmS8yLofS26r7VWWg2qtEoZmyiq1keNt5YUeOkxd0FLMp/yjCPEAJXoytqz60tvGYN7cLaYVjUMKs8fb6Bp8hcpFkz4FMWWl6EVvs+ALy1/S0YiRFuRjdVvlMLVIKCf3h4GuWvp2T6K/9djKqp+DcAJExIEG7wZf3USob8Q6alD640+nHX41SbXEC5AObp85h7eC7iU+MFz5QKVTkypN5e4bZdWDvN8ZppuWlCtJZTmKPyrovMRfjsgCVy+7CDxVukDovW1CXlOSlVVCt5VsKR5CO4VXBLuE48d15ZaDySfARfHVQzXoK9gq0qfSqRnpcOb1dvVSE9Mz8TG89vxOB6g+1GxFozBa5mXcXsmNmaxU0l3FzccPGVi/j5aZmR5evui/Lu5VnXu9ZcDOVzwf//Z10/Q0FxgVDfoJs3bdJS4ue/f9bsz+DTTLxNeSbyGUx/Qq4b8jIf3x/9HiPXqbunSxNlbvQre1fGxkEbGR85+VYyisxFMBKjUGSjoRsVsqI5fwMxMAYB/8DxniYfwtLKv5ZSHiAbkoZBDYVNQwutq7TGpXRLuPlr/1+F94vNxfgn+x+rDVa8p9M8tDm8XL1UYmI8e8DeMGRlL0DRu0WY0GKCQ2qXE1tMhMv7LoLEAf991HjymwL1Jqkx4r1LRxRE+eO1mp/o+9TDUhr21CmpMBAD5j05Dx+2/1BFlyuWijWZS9O2y86CrUErvLeuhXVn1gm1AOpJKoua4b7hggQyADyx5AnxPEs89qrlqyKiYgSk6ZJm8Z03+vOenKdiBlHDEZ8aLxw7pfUU9jfo9aODujed3yT0tlAk30rGM5HPYH4P8T0tYsGhpEPILshWceHpzGOT0aTqc1j1rEiB1ZLfSLmdgsNJh9mmb+0+pp50uF84+52UhtqqSivsGr5L81orMwYGYrBErYQgc2omDoyy1Ndm7Z+F5U8vFyKOYVHDkDxZdhLrV6qvSTKhjKeB9QayeQ2AXHe7fus6fur3k0BCOH79uJBiKguUudH/a/RfqlC6Y3hHFL1bxAaCAJZizDc9vsGVCVdY6DXvr3ksVOZTDJEBkTj1f6dQu0JteJg8EDs6FsfGHGMXlg/deFQoVwFHxhyxO31p5/CdwrhCZffevsR9VidW8Xi56csI8grCyRsnVdRTRxFWPgzrnl+nCnMJUWt8awmvuRndmDdIRc9mdbaMI6TyB/xkLWp06DrxXqS93oBw33AhMqhVoZZQaN4zYg+LxKj3pBxcXqFcBRBC0Dy0OaKColSecEFxAQ6MOsBSGRS0Zd/aKL4O4R2E/hAtKNlS3Wt2x+pnV7OU4sgGI1HwdgEq+1QWGsp4uBndEBkQiQmbJkCaLuHKhCs26yCuRlfWE2E0GFHTvyYK3i5gv5vKG4R4h6hSm/Ta0u+nz5vyOu0fuZ9xyH868RO2XtoqvK/VR0CZUFoFcUAeHK6kPyuj309jPmX/zY8PLDQX2jX6PHedykXQel3FchXRLqydQMnsEN4Bbaq20ZQwGdd0HHMKfdx8hM3/atZVlWKtv4c/gryCkDgxER93+hhuLm6afSGATK3lMw79V/ZHy+9bYuDqgZi1X37WrM2lprgT5dY7QZkb/WdXPotnfrEoNLav1h4/PS23X/NaLJTWZzQYcTTZwtt+POBxNpFeiToBddC5emfEJMagUXAjRAVF4ZPOspdzrwPXXQwuLARe8NQClURxZe/Kdgs7gKzYdzH9Is6nnRdUQAFtz0oZdlfxqYKYF2JgMphYnaKWfy2MXDcSh5IOCQ/L7M6zBTpbOVM5dKvZDUnZllw+lTrgveSIChHwNHkKs1/pZkI3Y5PBhFr+texK3FbyrCR4enSj4jesX07+wgwSpdZZ65zceXknlsYtVXGqC4oLUN2vuioHy0spa2H7pe0Y1WiU1VmoLzR8QdUkszdhL+YcnMPSVFFBUXY3PlejK07dOIWrWVdxMuWkXcaXp6snG8RBDb3JaMLWIaJhntlpJqKCopgMyTeHv4HJYEK4bzg8XT3RMrQl6zlRRkItq7Rk63O78DZL61Fk5GXA29UbLzWxFM2VToXynteKdPmCrhI7hu0QdHboOfLn+k67d5hXzafwFjy1ABOaTxCcRSXqBtRF3QDtvLy3m7dQg1H+NqPBiOENhrPaCH0WQ31C2XPGS4XwaLe4HSM/API1VKbo+M1PieTJyf8Onj4gM3P4H98urB0SMhOE4eOTW04WBh/QBQ70DMS2odtY+KiVY99+eTuOpxzHjJ0z8NhXjzFubOw/1gXP7IEyXahXkJ6bLgg8AbLBtqeICMhhbExijGaumdY2+LzgL/1/QXRYNOLGxqG6X3UkZiUi5LMQvPqnJcd87dY1/HDsByRkJgh0z3qB9QSPOKcwB889/pzQZUuvBS8XUCwV43bhbaF4GFExArUr1GYRDiEE3/b8Fot6iRRCCuo1jW82njGkit8txu23ZOoh37b/1aGvGBe7TdU22D9yP3sYfx/4O46PtTw8M3bNwKRNohxFiHcIDMSARUcXoZypHJuONKDuANZAZgv+Hv5WBfEW9lqoeu124W3svrIbHcM7IuaFGEEOwM/dD30i+qjoiXxkVPebuui6TFvIi4dZMsPV6CrcV8riIU2D0VGjB5IOoMmCJhjXbBzahbVDkbmIPT/Kjcn3v76abCaKI8lHkF2QLaQ4lIwaZRqSj9CW9llqN4KOuRojCLvRcwz3C2dRhVkys8iQj6ZrVaiFz7t9brMmU92vOn49/asmnfbLg18KZAHl9xiIARU9KrK6n9bfsTWUiJ+N7IhtoAQIIzHCw8UDtb5y7mxcivtO2TydehqDVw/G4mOLsea5NTg25hjcXdwFGhrTwQ+og/Lu5Vk+V+m5JGcn49SNU9h+aTve2/UezqWdY54InfBzp/B190WLyvJQCRoiarFoLqRfYBdZKyzmUSwV2ywwftrF4gG8v+t9fNTxI9QPrC9QAvnpUjSl4GZ0Q+uqrfFy05fxWZfP0L5ae9WN2rl6Z820Al+voLQ5/gHPK8qTJ28VWG7yf7L/saowOTxqOJ6s9aRVqqFSiZM2ABFC0LJKS3be2y9tF+bVGolRyBm/2vxVJE1KQpOQJnhnxzv4+e+fMaj+IIT7hqPQXIjey7WZNzye+/U5QZ6AR+w/sSrqIo3QiqVitAhtITgf6XnpSM1JVRUzeUkRRxE2JwwFxQWCRMjMfTOFY97f/T4upl9Ez5/lmhVNj/FKmbQYyV/38m7lkZmfaXO2MnUItHLOWnWQ1lVaC4NC+tXpx0YC2kuhsd/DdczTdd94fiP8PPzQr04/VX+HPdwuvI3UnFRNg60kBWiRIP65ZenF0JohwSt6KsGvtyNd3pTZVcmzEjac3YDM/EwMqDsA0nTHZgc7ivtu9C+kXWBSwrX8a8Hfwx//2fMfHE6y7P70YnSpLne+0jyasoBILyA/4HxCiwn4vtf3dvNn1vBWm7fQsorcGUcIwc5hO7H6udU25RP4TeHI6CN4t52o0GiWzDaNPi0+AnJkNGnTJJAZ1plDIxvID5qbixumbZvGZAeOXjuqYjLQtaRNPxS8nDPdTPkUDA1VeQM4eM1gq7pBX/f4Gr8P/F0YT2d838g0bJQeq/KBo8bj05hP8c1hS4qqvHt5oUOTj0byi/Jx8sZJkBkElzIu4ddTv2q21iuxLn4dTB9op2eaLGiiemDpGljz3vYm7BVC+z8H/Wlzopk98Cm05Oxk+Ln74a8X5SagEO8Q5BbmskZESpA4k3pGJUnRP7I/i4KUqQa+94XCngyxEkPqDxHWyutjL3Z/2KJg8uBlHugGtqTPEqw6vQqx/8SqZCDsgT5LWo5Oel660BTq4+Yj1BgAWSKme025IKvVt0AHoNNnEJCj1Z6P9RQ2OmtGv09EH3zfSy7+v7VdlvWY39NSUHdE8fROcd+MPk2PUCEkQF4YWqCiVXDA4ukz41tSwFWyBOhxRoMRoxuNRsOghnA1umJkw5EOCZFpgUqfUkRXi0aQV5CKXZI3LU9Iy3z95NdImpSEhsENMaP9DIF9UWwuttn4kpSdJBSBlEqNSlC5WHcXd0G9cPeV3XB3cRfUS+nNt23oNkjTJXaz8kaXhtD8jfr7OTl3qdysrAnJWQN9+Omarnt+Hb7r+Z0qDcAX497cZvGSq5WvBlejK2uau5B+AV3+1wV7E/Yivzhf0DVyFjpW7yiwhdY9vw7rnl+n6RlGh0XD1ejK8uj7Ru5D15pdUc23Gqr7VUct/1o4NOqQMMvAHvi8+ckbJ5Gel456gfUQ6BkIf3d/qwYluyAbAeUCWE6+vHt5VqSn15EykRoFN1J9XilDbCAGDKg7AGHlw1SbhI+bD6ZsmaLS4KfRIpVeUTpq7i7uAtlgfbw4m7hq+aqoW6kufov/TegLuFM4ajxzCnPg6+7LlIAreVZiz67WbAMaffO1pHlPzsP6AesFm6OUiqCoXaG2ICAZ6hOKZpWbMdt2OOkwvD7SnqNxt7gvRj/UJ5QxYfjCo7ULU6diHbwX/R7j59PF5FMN/OuVPCvh26e+tTm4xFHEJsdqGl1l2sLNxY1RtOpWqitvNAYTyAyC17e8jqo+FmncInMRRjWyTP/SohreSRMN7VhWzvLMys9CQXEBY2gAau90dpfZGNlgpMCd5gdfUNBroyU7fDeg7eqNghvhxcZqlgIvT8HfI5U8KyGvKI9FIVV8qmDLxS34LvY73Cq4ZZd+eTeoW6muwNMP8Q7R7N4E5Fx0oGcgYz7x1+TTLp/ik86foGnlpg6puh4adciqltLmC5tx/fZ1XMm8otp8aGrJSIxCCuPczXNovUhuAHQ1ukKaLjGlRy05AqVjY5bMOHXjFFyNrsL927VGVyx4agGyC7LxTJ1nVE11PJQaOEZiFKIAKncNyPo4R0Zr96Y4ik2DN6no1dZQUFyAEykn4GJwEZwQGk3yozYp6GbJK4dqUZgDPC0d//y98/mBz4VoeULzCarP26ob3A3ui9Gf230uEyPiWTW8QeKNS3n38nhv13usjZuGu9Y8faph4azQSIszX7tCbdVriZkyc+HESyfg7uLOHppP9n+CHZd3YFzTcQAs3hXN/WsNdrA1tQhQSx6vfW4tmlYWpw0ZiAHZ+dks3wtAoJ0Ccjj9fe/vhU1m46CNeK3la8VpumwAABReSURBVEJDm9Zw6Pk95mPtc7Zb63l80vkTbB+6XTiPzv/TNn788Are6A+NGoqlfZay9noqhcA2PpP6PAF5fXhtkzvBhbQLQgu+rR6KxccWIzErkaUm+DRKn4g+6B1hv8ZA0bRyU83GLcBCnYxNjlVt5JS8YDQYkXwrmeX3eaFD6ulTKYi8ojy81caiGtq1RlesftbSLEgRdz0O59LOCc/t7iu72Yxek9Gkirak6RLS39DueA7xDhGiR/6Zrulfk9Xi1jy3hm1Qd4IuNbrg6Ui1OB8F73DRiCk1JxXeH1siO1t2RNk/ERkQKRh4io3nLE1eMzvNhDRdwoTmE/BCwxeE5//UjVMoKC5gz5u16XH3gvti9HmePpUeWD9gvSy0RFM3XPs8zeFSj4ByrpUpEjejG5qENNFc9HsB5Uvz0NIFebnpy0Ihir9ZDiYdxOnU0+hcvTOmbJmCpXFLHVJzVIJuGPOenCcYYq3ctYEYhHNoF9bOIU84MiASn3T5RPgspdDyEsljmoy5IyP2WqvX0D5cVr4cHiV/n5LTTWFtoEawdzArHlf2rqzqtKYbKwDBO59zcA5ebvay6u/Y8kop+KK58tysgWq6Kz1bZ4Fex6rlq1qlzRqJEWMaj2HGUku7iTpfZ9POomN1y/S5vhF9NRsWKYONNnwB4samnFjGf05rSJK3mzf2jNjDJE60BhAB8oapFRHeCxoFNxIUcl0MLjg/XpaB5tVIqUOk7B3RgjVxtKnbprL/jqgYAUmS8GnXTzGvxzzhuEXHFuFi+kU0Cm6EN9u8aXce+N3AfvtmKYDSM7cO2Yo2Vdug87HOTJeHejC8hxOfKufXaJqFH7bMw8PkgcMv2vaQ7xS33rylWQ+Iux4HQG4uoRIG/R/vb/O7+OEOtHOzcXBjgb5oD14mLyROTERCZoLwsA1bOwzdanZD1xpdsenCJgByUxSf703PlWd43k0ERL1JZ032scdm4M+RN/pXs67iy0OyAFVSdpIq5KahcaPgRnin3TusiWjn5Z2Ye0jdDGdNJI3i444fa3Zz2kIlz0roXL0z1p5Ze8eTmuwhdnQscgpz2JqMbzYelX0qo5JnJZWAm6erp1AU5J8XSi6gNbWn6zyNcX+MQ71K9XAi5QTG/j4WRoNRbnrk1tgW28cayAyCaW2nsXN+vdXrrEHpdsFt+Pr4IjosGgREpX1fmugb0VfVB8MPO6eg52RtQ+pUvRN61OqBtWfW2mTzAHLEvD9xP1ovao0h9YdgaV/10HMjMSLcLxwfdfxI6FFyFu4reyeiYgQMxICxv4/F8HXDkZmXiVCfULwX/Z6gbKjMJ9MctSPNUPcKT1dPzco/rcxvG7oNC3ot0PysI2JfWqkd5Y3TtUZXpk2+8OhCVPm8Cmbtm6X6XLG5WBiXV7dSXXiaPJk3q+wsvRM8V/c5HB97/I7ZE9ZAC/XKNBUFzZV+1f0rxLxgqTHEp8YLao3KyCWvKA9L+yzFwqcWYvQGkaOvFIlzBFPbTFXNq7XFPa/uVx1danRhxsTZsriNghuhTdU2bBOm3nvSpCSVx688T8rkeuqxpzChxQQAFqFCN6MbTqeeRmRAJDv+pd9fUm2q9rSMlLUuKn9y7Noxlkrp8VgPdt0pa8wsmVHDv4bT18sW3m73tmb092rzV5mcNyA7F77uvlbnR28ZsgUTWkzAzuE7MaDeAM1jKLxcvdgaUjlxisW9FwOQ03Knb5wGmUGw7dI2p3fm3lejbzQYhe5adxd3uLu4Iy03TaDlKVM+LzZ+EdJ0yW43aGmCeryvbX4NXx/SluWlBTZeWVEJntpHMbHFRHzY3pJDHlJ/iOoG0WIS+Hv4Y0HsAvi6+0KaLqFhcEOYjCZceOUC26Tuts5RsVxF1Aus55C2jyOgFEZrNQGatmka0pQN9gbUImJUZoEiyCsIQ6KGoGFwQ9XaWhPd0wJl3+xN2ItQn1BI0yX81E/uHLc1XPti+kX8nfI3iyC05JOdAXod5hyYg5s5N9F8YXPNNCQPKki2/ux6lvenaQwq5sdz8h2ZkcCjfmB9gYnTq3YvNnv6duFt9I+UI2EtcTejwYj1A9YzMcD7iTnd5jB5cUBW/H2t5WtW9bscAa2P3Mi5wTZspcOy8pQ869lIjCyrcSLlBL576s5rGbZQ5kZ/QvMJ7L/dXdyFaTJuLm5IzUnFl4e+FMKa8u4ys8DRBo+ywJcH5RTD/Nj5GLdxnOYxFctVROLEROx/4c4kICK+jsDkVpa+ghfXv8jCYVtwc3HDlotbNIc5RwZEWg1P7wcY192KKmh0WDQuvXqJdfNSKD1BCRKrc7zR+g2hkYuO6aPeqz3aLr8500227Q+We45GOVpFbR6FxYWs7uCsyEgJWrfqGN4RxVIxjiQfUdUaKLGAgs+p0w5Vmt7hN9a73dh93X0FQ/Zb/G9oWaUlxjUdh4VPLWSRucloYg4cH31FVIwQ9J4eFHi5emFau2kqh+NO0LdOXzQJaYIpraagddXWeLvt21jUW+5mz34zG1lTsxgtutBcqLk+zkLZDkYvXwWfd/scLgYXvNnmTfi6+7Jdb0BdOSyiDAOalwYsnbDWhlncD3z31Hd2JWQNxIBQn1D4uPnYHcumxMqTK5Hxhmy8lY00tsBvojx2XN5Ral7n3YB2tVpLgXm6yto/ytQaffCo1gxgGeSt1PGn9xTNX9vLxfMza7W00rvV7IaMNzJsGqact3JwZMwRDGswDBlvZNyzfr01UGNQsVxF5pEra0NKyXJ+LWlBNjIgEhlvZAgjLPPfzsffL/3t0Hm4Gd3YVLDdV3bjUNIhQdzMxeCCr578CjX8a2BAvQEwv2tG7Qq1mUfPbzb/dhx+8TBmdZ4FAzHggw4fsPqTl6uX0JdS078mSy81DWlqszHzblCmRp+Gko8HPM52/WntpmFs47EqjROeuhXkFYQNAzYI7IL7jZ6P9cSpl+V85520WPNoGtIUoxuNZmEvj4TMBBbhKJE8OVnFOqH6/NaGyVhTmbxfoA/7nXqV1NOnHnRN/5owS2Z0r9ld8PIBuZPz+mvX0blGZwxvMFzIV1PwhrBTeCdkvJGBy69exrExctpIqRBp7ZpQeJg82HfaO/ZeQA36kWtHWJpTOaNBy1loGNQQkQGRaBxi0Y5SnqeBGASxPS1Q6u306OkYWG8g23BTc1LxZTfrk54IIWxWBaDdo/Iog97fNfxrQJoulYrNK1P2Ds0nv9TkJfxnz38wpvEY+Lr74puelm41rXCmnKkcejymbhN/EHDxlYuasq1KaPHu1w9YDy9XLyZNQBFQLkBTstXF4IKnHnsKQV5BGNt4LF7f+jp7jxr1yxMua8pIX5t8zebc37LGimdW4GL6Rau8emsI9wvH9OjpbDoYjba0hl2YjCZU8qyEo8lHMarhKLSu2lp4v7J3ZXi5eiH+ZjwOv3gYjYIbwUAMzAieHXf2rjWbShs0PZZXlAd/D3/EjY1DkFcQAmdbKLVa9Fw3FzerlOYnqj3BpBc8XT0xq9Ms4R6j8HP3Q/vw9jg46iBLGTUMaogfT/yICh4VhC57a2hTtQ2W9V1mVwX1UcKFVy6o5kSURnrnnow+IaQbgC8AGAEslCTpvzaPL/kBGXkZSMxKtFlULI0fWxpwtHki5XYK+kf2Z8UaQH4o39/1PqPBPVbhMYSVD8PmIeoWfWm6hITMBOYhDY0aiovpF9lQDMrUqFiuoiar6UF7uMqZyt1V6sPV6MrWYEzjMVYZFTyoKFnc2Dj2WkTFCHSv2R1vtX0LiZmJqtoBoD3h60GBt5s3Yl6IYRFk/cD6gpKkv4e/Jl30r3/+shpdjW82Xki3Da4/GI9Xehx5RXkY98c4pjZJC4v8nOWJLScyiWcl6UAL1f2qO9Qj8ShBaz0cySLcKe7a6BNCjAC+BtAZwFUAhwkhv0mSpO5Vpp8pMeTUS9MSrKpdUe501Zpz+TDjTOoZVSNSOVM5loP/ofcPGLFuhKqbsW3VtqzYyRu4QK9A9Inog/mx8zGxxUS83e7tUv4FDwbyivLw3s73AFiEqTYP3mzTgJxMOYlzaeew5vQaeLl6wcXgghMvnWDGryyov6WBFqEthP/nC9XWBM4OvHDAqpSGUhQu2DuYpeHoAHRAu+HMQAwsvaQlV6Dj7lAaRv9evrEZgPOSJF2UJKkAwHIANtszqfGiN50WcyPQMxCfdvlUcxTZvwnLn14Oo8HI0hvU8FBviqJbzW7oVkO7COzj5oNAz0A8Xedpzbmg/0YUFheqKJOda3TWbKqhoMVfo8GIEO8QjG081mnU0wcJjvymxiGNNTtj7wR0/rA1bL0oD3q5G9kEHSJ83X0xqcUk+wfeAYgjDUSaHyTkGQDdJEkaVfL/QwA0lyRpnOK40QBGA0DVqlUbX7lyBadunMKGsxvwemt1vvDfijWn18DF4ILtl7YjISuBTZNKzUnFZzGf4YP2H2D538sR6BWoOTRdhwxJkvDmtjdRwaMCprSe4tBn/jz/J5b/vRyzu8x+aL16R3Ek+QhG/TYKn3X9zGrj291gzek1MBlNuJxxGc0rN1fpPPHIyMvAx3s+xgcdPrA5ElKH4yCExEqSdG+7Nf2u0jb6PJo0aSL99ddfd/X3dOjQoeNRhTON/r2kd5IA8Hy20JLXdOjQoUPHA4p7MfqHAdQihIQTQlwBPA/gzmaZ6dChQ4eOMsVdV7MkSSoihIwDsAkyZXORJEkn7XxMhw4dOnTcR9wThUGSpD8A/OGkc9GhQ4cOHaWM+z4YXYcOHTp0lB10o69Dhw4djxB0o69Dhw4djxB0o69Dhw4djxDuujnrrv4YIdkA4p34leUBqCeCPxrfWRFAqpO/82H57fp6Pvjf+TCsZ2n87tL63tqSJHnbP8wBSJJUZv8A+MvJ3/ddKZzjw/KdTl3Lh+y36+v54H/nA7+epfG7H4b1fNjTO+sf4e8sDTwsv11fzwf/O0sDzj7P0vrdD/R6lnV65y/JSfoRjzr0tXQu9PV0LvT1dC6cuZ5l7enrWqvOg76WzoW+ns6Fvp7OhdPWs0w9fR06dOjQcX/xsOf0dejQoUPHHUA3+jp06NDxCOGejD4hpAohZAch5BQh5CQh5NWS1/0JIVsIIedK/u1X8noEISSGEJJPCHmN+x53QsghQkhcyffMuLef9XDCWevJfZ+REHKUELKhrH/L/YYz15IQcpkQcoIQcowQ8khOAXLyevoSQn4lhJwhhJwmhLS8H7/pfsKJtrN2yX1J/8kihEyw+bfvJadPCAkGECxJ0hFCiDeAWAB9AAwHkCZJ0n8JIVMB+EmS9AYhpBKAsJJj0iVJml3yPQSApyRJtwghJgB7AbwqSdKBuz65hxDOWk/u+yYBaALAR5KknmX5W+43nLmWhJDLAJpIkuTsZqOHBk5ezyUA9kiStLBkFkc5SZIyyvo33U84+1kv+U4j5EFWzSVJumLtb9+Tpy9JUrIkSUdK/jsbwGkAlSEPSKfTk5eUnCgkSUqRJOkwgELF90iSJN0q+V9TyT+PXIXZWesJAISQUAA9ACwsg1N/4ODMtdThvPUkhJQH0A7A9yXHFTxqBh8otfuzI4ALtgw+4MScPiGkGoCGAA4CCJQkKbnkrWsAAh34vJEQcgxACoAtkiQddNa5PYy41/UEMAfA6wDMpXF+DxOcsJYSgM2EkFhCyOhSOcmHCPe4nuEAbgD4oST1uJAQ4lla5/owwAn3J8XzAH62d5BTjD4hxAvAKgATJEnK4t+T5PyRXa9dkqRiSZIaQJ6124wQUtcZ5/Yw4l7XkxDSE0CKJEmxpXeWDweccW8CaCNJUiMA3QG8TAhp5/wzfTjghPV0AdAIwDeSJDUEcBvA1NI414cBTro/UZIm6wVgpb1j79nol+TgVwH4UZKk1SUvXy/JWdHcVYqj31cS6u0A0O1ez+1hhJPWszWAXiW56OUAOhBClpXSKT+wcNa9KUlSUsm/UwCsAdCsdM74wYaT1vMqgKtcJP8r5E3gkYOTbWd3AEckSbpu78B7Ze8QyLm505Ikfca99RuAYSX/PQzAOjvfE0AI8S35bw8AnQGcuZdzexjhrPWUJOlNSZJCJUmqBjnk2y5J0uBSOOUHFk68Nz1LCm0oSUN0AfC388/4wYYT781rABIJIbVLXuoI4JSTT/eBh7PWk8MAOJDaAXBvKpsA2kAOP44DOFbyz5MAKgDYBuAcgK0A/EuOD4K802cByCj5bx8A9QEcLfmevwG8ey/n9bD+46z1VHznEwA23O/f9rCuJYDqAOJK/jkJYNr9/m0P83qWvNcAwF8l37UWMkPlvv/Gh3g9PQHcBFDekb+tyzDo0KFDxyMEvSNXhw4dOh4h6EZfhw4dOh4h6EZfhw4dOh4h6EZfhw4dOh4h6EZfhw4dOh4h6EZfxyMDQsh7SsVHxft9CCGRZXlOOnSUNXSjr0OHBX0A6EZfx78aOk9fx78ahJBpkDsbUwAkQpawzQQwGoArgPMAhkBuGNpQ8l4mgKdLvuJrAAEAcgC8KEnSI9cpruPfBd3o6/jXghDSGMBiAM0hC30dATAfwA+SJN0sOeZDANclSfqKELIYcvfyryXvbQMwVpKkc4SQ5gA+liSpQ9n/Eh06nAeX+30COnSUItoCWCNJUg4AEEJ+K3m9bomx9wXgBWCT8oMl6oetAKyUZVIAAG6lfsY6dJQydKOv41HEYgB9JEmKI4QMh6xPpIQBQIYky33r0PGvgV7I1fFvxm4AfQghHiVKmU+VvO4NILlE2nYQd3x2yXuQZG3zS4SQ/oCsikgIiSq7U9eho3SgG30d/1pI8ji6FZAVMjcCOFzy1juQpxTtgyjhvRzAlJKJTjUgbwgvEEKowmbvsjp3HTpKC3ohV4cOHToeIeievg4dOnQ8QtCNvg4dOnQ8QtCNvg4dOnQ8QtCNvg4dOnQ8QtCNvg4dOnQ8QtCNvg4dOnQ8QtCNvg4dOnQ8Qvh/mYi9tYtEr1IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "xyitI5oNJNBm",
        "outputId": "eb9b678f-9c97-453f-b09c-a4860d0d1250"
      },
      "source": [
        "# Resampling the dataset using the Mean() resample method\n",
        "timeseries_mm = time_series['wind_speed'].resample(\"A\").mean()\n",
        "timeseries_mm.plot(style='g--')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "jDYkRLAkJQP4",
        "outputId": "6b2dc047-a57b-456d-fc72-f60ca3c57018"
      },
      "source": [
        "# Calculating the rolling mean with a 14-bracket window between time intervals\n",
        "time_series['wind_speed'].rolling(window=14, center=False).mean().plot(style='-g')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6BBYEcKJUd_"
      },
      "source": [
        "# In the coming sections we will implement time series forecasting on the same dataset"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdLBj6EsJX3y"
      },
      "source": [
        "# The series module from Pandas will help in creating a time series\n",
        "from pandas import Series,DataFrame\n",
        "%matplotlib inline\n",
        "# Statsmodel and Adfuller will help in testing the stationarity of the time series\n",
        "import statsmodels\n",
        "from statsmodels.tsa.stattools import adfuller"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BONAV-0AKIEf",
        "outputId": "ced62d6e-8a8b-4295-9968-6b3c274f03f7"
      },
      "source": [
        "time_series_train = pd.read_csv('https://raw.githubusercontent.com/rjrahul24/ai-with-python-series/main/05.%20Build%20Concrete%20Time%20Series%20Forecasts/Data/DailyDelhiClimateTrain.csv', parse_dates=True)\n",
        "time_series_train[\"date\"] = pd.to_datetime(time_series_train[\"date\"])\n",
        "time_series_train.date.freq =\"D\"\n",
        "time_series_train.set_index(\"date\", inplace=True)\n",
        "time_series_train.columns"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['meantemp', 'humidity', 'wind_speed', 'meanpressure'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Wxm-HHp-KL-W",
        "outputId": "86c3f2e8-6fe5-46cd-f299-c7cd8832c8f5"
      },
      "source": [
        "# Decomposing the time series with Statsmodels Decompose Method\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "sd_1 = seasonal_decompose(time_series_train[\"meantemp\"])\n",
        "sd_2 = seasonal_decompose(time_series_train[\"humidity\"])\n",
        "sd_3 = seasonal_decompose(time_series_train[\"wind_speed\"])\n",
        "sd_4 = seasonal_decompose(time_series_train[\"meanpressure\"])\n",
        "sd_1.plot()\n",
        "sd_2.plot()\n",
        "sd_3.plot()\n",
        "sd_4.plot()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U1nHXV0pM9cp3dlJaWcNvh9K57gP7ymdrguJuTswusdm3YEiY3XNKq2rYoo68fj6QZveYM+XJX47y+c5TrDmWqW17Y10Ci5fv4NJl22yOn9m/C7V1kvTCCjxdG0/UachQXXEwnSNptpPns4oqHIYZnEy2yWX67Mf2juGdEwrKxcnya1w+Wung9ZNJAWJO5lu8X/DGZoY9+QcvrYpj6osbAGUu0K2fxHDTRw1bYU3BPK8B4J9f7ufyt7dzz9eW2XB/m9rL5ryR3QMY3SOAVy8fznl96jP77v/uEDd/HMOFb25p0G10+3TLa368vX5i5y8H0nhMF2C2toh83V1w1bXnFzePZ+Hwrtw7tx+vXjEMoMkZbumF5Vzz3k4Sskq0jsNRx3WmmGNrzk6Cnxopx6JXUPl2RlCnclunlFNJZQ0xyXnc/LFSJ7J3F2+GR9oGmX+9czJxT8/j+9smct3EKIsO4YOtJ3jpj4aNJus0a4A7Pt/bYAyrKZgVlBDQLcDTZv+uE3ksXq64TY9nWLqhHvrhULNjkIuX72DOq5sAbJRxRlEFO5tQweLL3UqVtHvn9tO23Ti5J+9fN9rmWH2/8eWutrkvzR26vttPsqNEzeizTpuSjVuqjs7nvbaJ536rb7Oyqhpu/3wvly2zNdDGPruW6x0oemsk0qKvOVOjp7mcEwqqX5gP0cFeACwaEcHcQYrFtL6J2Xr/XZ+gvXZ1NrHqSCZrjmU1cEbTuP/bgzbbDlmVA3pgfn++uHm8xbZgb1e+/ftEhnTz44PrxthcI7+smmd/c+zGGRMVaPHeOplBn8Jqja+Hs4UndGBXX15fPILbp/fmkhGK4l80MoIrx3V3eA0z//rqAJvjc7j/u4Oau2h3cr5NZpAjGnoYDqUUWmQy3fiRogQ8XJwazGYES0+vTTo/kJBlvwN5d3MSY55Zw7H0IpYs38H62Czq6iTHM4ttznnsp8MMfmyVxQju5zsmEeztZnHcPbP7MjjCDzdnJ0Z2DyDMzx29l/mJXxqfE7UxLovs4kqLWMWqI4rF3pgruCH0Lj4nE/QL9WFIhJ/dY4sra3j7mlEE6eKTzzVwj5pHfVJKCsurbdyg31plO5ZV1vLRdtsKGtdO6GGzzdPViVkDQrX3QyL8mdE/lIuHd7U47qf9qZrBMLan8sykFZTzxtp4hj3xhxbXOhPMv6V+2ourk+NuN8LfQ3t965ToRq9vbsfYjGLe3pTEZcu2cd+3B0hV53qWW42UzJ6X7Um5rI+17eOsHYYV1bUWoy19vLo9OCcUlAB8VL+9i5MJN+eWz2GxHo2dCXGZDXfEQ7spD/uEXkE8cv4Abbuna/1N4OxAnoZcEoMddCKO8Pd00R5oH3cXbeLnjZN72o2HuLs48ewlQ+xe65Yp0ex8yHJ6mwD+t7E+frJsQ/3r2jrJG2vjbaztmOQ8Bj+2ivVx9g2FC/+7hds/t01n9mrCA1Spe+AyimwnbX+y46RFlpqZF1bGkV1cyRO/HGF7Ui7Xf7ibdzYnMefVTSz6v3q3VWWN/c7U09UZk0mQ+OwCFo2I4MfbJ3HnzD4Wx3QPtB2pNEZaYQVjnlmjjT70nElZInM5JieToLZO4mRqOP44d1AY715bP1LZnpRroXj0WXfb1dHBZztPMeyJP5j0/DptX0ZhBSWVNQwI9+W1K4YDkJRjZQBcOJBXLh9GmJ/SoY+JCtAUkL6TP69PsOayfm3xCIv2zS+t1txo5VW1bIjL4qL/buGV1ccpLK9udOpHU5CyfhRqxrmBNgzwrFfwt03rbbGvq53J8tZGXMzJfL6OSbHIaNZTqHNpX//hbpvnztqRG59Zwv3fKYb24Ahf3rqyfZfnOycUFNTf/BKJm4vjr3XF6Ei+vnWCw/1nYnGaKa2ssSimao+X/zKMD68fq72/6bxoRvdQJsd5NMH3bOaphYN4Y8kIQLFwP79pnI2V3hBBXq58c+sEzHrQz8NFm/TZ2JyH8dGBNtuqauoI9XVn8Zj6cooxJ/PZdLx+NGuu6AHKjPhXVh/nyV/su2O3NdOK3atz41o/0J6uToT4uFlYldU1kmGR/uz7z2zin5mvdWBmt5yearU6ye7k+s9Yq460zXG97Ym5PPDdIZtzn7hokPbaySR45Yrhdt19E3sFNf4lm4G9FPTq2roG3UxmflVHqG7OJmrqJC5OQuvA7BkuYGlhn8wt49/f1Lu09dlyZveVef6XvhrGL6rxdfv0XkSpnhHrUfHFwyNYNLIbo9RnZsGQcG1ycHxWCX1DvXn8woG8vniExXn62G1VbZ0mx75TBVz3wW5ySiwV+htr4/nbJ3t48Hvb37QpmHWySaehHBnBL/1lGL66dvWxSjzZfP8M4p6exwfXjeHYk/P4+7RelFbW2I0LOVKueVYu7YSsYvJLqygsq6a2TlokiQV7uxKfVaJliz56wSDt92gvzhkFZXad/bQ/DQ9djERvuby+eDhLLx3C2J6BFhlyera0wrD+xVVxNvEvPU4mwSUjImxm/z9ywUD+OqEHA8N9m/xZcweFaR2xs5Ngoq4ahTWzB4YSGehhse3WqdH0CfXhyYWD+fu0XoyPDuKykd34+tYJXDC0q4MrKXxx83j2PzqbYZH+LBmruPzMD9UzDkZYgEXw15xR19L6gbV1kgverE831pfa+eXOySwZW68oLx8dyeTewRzPLNHcT1W1dXQP9CTAyxUXJxM/3zFJuY6uoyquqGbp77FavK62TmquLnNJLVCs5SXv7OCHfbYxsBn9uzTp++hHgHfO6G33mMER9ffH4SfmNng9e0kDT/96lBkvb2xymx/PLCG/rBonk9A63NeuGM5TCwfZHGs9BWGrLgMvX2e9707OU2tJ1m8zJ4dsUiejRvh7OEwUMCvIsT0D2f3wLK6bGMXIHvUKXwjBdZN62jxjHg7ip7sc1Jp8ZfVxVh7J4IsWxqjMI6is4gqtyK9+lAQQHeLF/64exaUjIyyyIU0mwSuXK3HfpYuG4GQSuDk7Mb1/FzxcneiquoMf/qFh5Rmj+27fxFi6Tl9bE8+Ip1Yz7Mk/mP3KRgtlt2CIZTUQ9wYM/7binFFQZkxC0D/Mh3/oHu4XLh3KleO6s3B4hOYLNjUhPdM6ZbkpVFTXNjov5OW/DLP7+cMj/Xly4eBmjaC6+LozOMIPJ5PgpvN62uzXdyKPnD+AD64ba7HfnPAQ6uvO/fP642QSmExC88k3hBACf09Xfrp9Ek9fPJjHLhzI7dOVdrfOBtJTpnP7mI9qafC110O/2Y0jLR4TSZC3G88tGsrBx+cwZ2Aof5/WS/u+b29KoqiimqqaOouYgL+nK4tGWi4/tvT3WP63MdFiW5ifO5eN6qZNY4CGJyY3NKrXo7eu75nTj3/M6E2or5uFC3hir3ojxNvNma9usYxh6llrJ5a6VR01NHWe2sojGRw4XYCzyaS5R/08Xbh6fA92qe5cs4vYevTeQ+dSy9JNOD2UWsi81zZZxO5euGwobs4mrTBxRICHQ5en/vkJ8XFDCEGwl/LZDY38W7OEVVMwj0ce/uEwD/9wmJT8Mpu5RffN7ce8wWEIIehm9X0XjexG8tLzWTzWNuY7rZ9i9Hwd03Bm7D++qJ8E/v1ey2P1Jc2Sckotigvo7zNwrNzbkg5XUEKIZCHEISHEfiFEjLotUAixWggRr/5vcmEoV2cTzk4m/jmrL6B0VJePibSJmTQl1mSeW9EcXljpeM6CGetRTGPcNcsyThHiozyIk9XRkruLE4nPLtCSGPT8ZXSkZm06mYRNgNb9DOJ1epxMgusn9bToAG6ZEs38wfUpvqN6BBDi42ZRVcNc0UPvWk3JL9NGK0IIpJS8uznJwn/eGHo5fN1dWP7X0YT6unOVLrnj+d9jqayps0mrHxrhR0ZRBX8cyaCiupbP7GQeRvh70NPK3fGvrxwnZzQnLuriJLTszbvn9GPnQ7O46bxopvdT1j0zWdWZHBcd5DDwnq1mBOpT0Z3U85vrznZ2Eto5Pm7OCCHo4utO7FPzeFuXPh/71Dze/etoooI8OZhaSEZhBZlFFVyhZvyNVBVIaVWtxcT0AE9XwvzcqaiuI8DThRBvN9xdnDjw2BztmKHd/ByORk0mwR//msJHN4y1ux9gziD7KefWXDLCZo3UZpNaUK4lK5i56aMY/m9DvbHz5MJBFnULu/q54+fhwrR+ja9xF9lIvPLEcwsA8HZ35uvdp/nfxkTyy6q5clx3vvu7/TCHfgQ1ITrIoq/q4uu4YHRb0eEKSmW6lHK4lNIcYX0AWCul7AOsVd83CbPicTIJDj4+h6d0RTj1+Ho0Hkx3VGOuIeKtMtTsPUy9Q3xstjXEXbP6WljQl49WrKpPbxrn8BxzdpObs4n/XjmC/mE+hPi40T3Ik1cuH6bFOppq2beEhxYMYNnV9R3XW1eOxMfd2WIe1mPqUiPmeTbpheVMfn49L6yqn7i491Q+T684xn3fWaboN4SjUejgCD/mqZ1UbZ2kurYOV6ug9VXjlba75ZM9Dqt+PLRgAFFBlgqqoazR5rhHDj8x1yI+aab+3oaFw7taxLUcVZ4oLK9mW2IOgx5bxS61/JN59KFXvFIqk7kbmqieV1pFX3W+kz4G5e7iZDFidndxYtbAUF76yzCkVAw98yKHM/p3IdDLfow0zM+dk2rKfJ9QH83b4aX7LX++YzLv28lsNdM31Adfd/vxMYB/qkkpgV6u3Di5J3semaXtM0/58HBx4h9WyStAs1eSnrR0HfFW2Z2xVun41iNEIQS7H57F8mts0+Lt8dENY/n4hrF8cuNYll01kh5Bnmy+bzqf3TQOIQSjegRwPLOE+747yNLflWdqat8QegTZjyUl6Qpb+3m6sPm+GfXvHcQd25LOoqCsWQh8pL7+CLi4sRPM9eH0fntfdxeHWXBNmQTXUBzJEdYzu/Vzkh45fwD/uWAgfp7N/6H1wedrxkc1evxjFw4i/pn5CCE4r08IK++aolnxi0Z2o4c638K3HW66Fy4byqtXDCPMzx0fdxdyiitt3GHmSb1mo0Af77nufWXORpKdqvB69KnFDbkj/nfNKKKCPCmprFFcfFYjqKaMrl2dTZp1Gepr2eHOHhhqe3wzskPdnJ3sukhdVDmdhOD1xSO4dmKUts96lG2muLxaq7W4/7SS3FGutrU+rvLRtmQGP7aKlAYmksdmFPPS5cP44ubxTbKmzSP9ZesT2XMyHzdnE29fM8p2IvKVIxjbM5DeId6aey5A94w4eoZbgpM6ylp791T+c8FAgrzdmKP+XuZRixD2sykTmpBY8tf3d2nl1ZqCjx1l6upsanSyvJmpfUOY0jeE8/qEMH9IOBvvnU5koKe2MoK9ShNjogItlM0i3WjR7Gr/uIFRaHvSGRSUBP4QQuwRQtyibgvVLVKYAdg88UKIW4QQMUKImOzsbB65YCDJS8+3eGgb4uW/DOf8oc1bEqCsqoajaUUNLhLmZFVlQe/amT0wlBsn28aJmkKorkMIa2BtJjMmk2iwo/3HzD48cv4ApvZp3JVwplw+OlJzP/q6O7PzRB7DnviD2z+rTxM3PzDWbqf3tpzQKjvYm1Sr5586q7cxf7mvhwu/HkynvLq20c7goxvG8v51o/nSKtbTN9SHsVGBvLlkpOZC9nV35p2/jub1xcO148b2DLRZ/qUlmDsb63sMlFH2S39RAur6zl1JN1a0vXm9r0FqgofeSDPHIhpbzt3bzZkJTcw0NMejdiXn8fH2k1TW1OHiZOKh8wdoqeCDI3y5YGhXvr51AiaT4KbJytwffw/LRIIATxeCvVtnSZG+oT4WyRxvXTWSlXedpxltUiqKzNpVaG/qgTWbjmfz3/UJjbpPw9TnuZUrFdlgXQjg2gk9CFQTgn64bSL3zO7Ly5cPY9fD9VNDrhgdyZS+9f2Ct5uzNiWmvWnfWVf2mSylTBVCdAFWCyFi9TullFIIYeN3kFIuB5YDjB49utkVovqF+fDWlSNZcbDh9YtqautwdjJRXVvHwEeVmflLxkby3KKhdo+3tlj0rh39/Kbm0hSl1BzC/Ty46bzGJwK2Nsm61WxXHKqfaGtO/W6ohFNOSRVpBeV09beN4X339wlEh9S7LdwbGSHrF62stlPG5+CGtpsAACAASURBVNMbx/HCqlhm9O/CVN3D+u3fJmjGgruLE1//TfHlZ6vzTsxpuOYR77iegXxwvWOXVHMwu0FdnO33al39FbkWDo/QEnVKKmu0NOENcVlU1dRpK82WVdWSnFNKVLCXlj2YbTV/ZtO905n72ibKq2v5/GbHLmV7WM9Je2hBf0CJ363/9zTu/+6gTabi7IGh3DS5J7dNt9y+7YGZyDYqVeriZKJ/mK/NqN465f+1NfH0CvFmfHTjCvrpBhadXDi8K9eM78ED3x+iX1jz3P3NRX/9N5aM4EKdUT6iewAj1HWfuvi44+PuTHFFjY17fP+js1vFwGoJHa6gpJSp6v8sIcQPwFggUwgRLqVMF0KEA2de1sEBX94ynppayZBufgx7Qika+fdpvaipreOdzSdIK6ggu6SCEzn1ro8Dpy2z++76ch99w3y4bVpvm5nb+hFUU9yKjhgQ7suPt09iUNemp6B3Rp65eAh/1VVjBugW4EGFmtnXWOXv815YT/zT8y229Q/zYVSPQIvlRRobQc0ZGKqVxbG3TPnkPsFM7jPZZvvoKPvZjWbrfuFwxV1i/t1H9Qg4I8NEjzm+6WgawsRewXx64zjGRweqmZ1KNQ/zhNv8smp2ncizyKL8y9vb2f3wLG3id2qBkmm3ZGx3bj6vJ92DPDn0+BzKq2vtuqMa44ZJPXl/qzIn8JYp9da8q7OJV68YbnO8q7OJRy6wXayxOZmtLcXX3ZlFIyO0KRN6D4RJKMp78fIddhc9zCqu4D5d5ZiGRqJOQjA6KpA1d09tRentE+7nzoToIK6Z0MMmbdwaN2cTxdjGpVvTxdpcOlRBCSG8AJOUslh9PQd4EvgZuBZlGfhrgZ/aSgZ71tD98/qzPi6LdzafIKe0kkut6lkJAb8fSmdtbJZFSZbbpvWmzGqpBr21f6ZpmvYmdp5tTOkbwpEn5vLmugQtdbt3F28OqBWuGyuCW1snbWbJmzsvvZXXWFs/sXAQQd6uvLU+kX5hZ670x0UHseIfkzXlMal3EMuuGsksO/GolmKOzzU0mp6sZv9dNqobubqafjP6d2FdbBbvbUmyqO6QXVxpMYH2mxhFaU/tG0x0iDegdFA+LeykHlzQn72n8rmhha7t9kQIwSuX2ypNUDIMG1rO/qNtyVoFcFDqcA4I97VY8dbFSVBdK/F0a790bSEEXzQwDUGPWSF3RDq5Izo6BhUKbBFCHAB2ASuklCtRFNNsIUQ8MEt9366YJ8zZy+QL8XHj75/ttakXBpYL4H1x83icTIKfbp/EXbP6NGnu1Z8BLzdnws2Ti02CfqE+5JdVs/poJpUORlAjdXNbUtVYwKTeinER6Gkbm4gKbjgF183ZiX/P6cenN47j0pFnnlIMMKirn6YkhRDMHxLeqqWzzKni1vEZRwR5u+GvxqP6hfnQP8yHDcezLUZQoMzRMWNO+/ZopVGfi5OJH2+fxEXDGp703dm5bHT9FI4Hvz9kMfkVbDPcSiprCLNKnnlwvpKJG+Ld/unaTcGcwdnec8UaokNHUFLKJGCYne25wEzbM9qWCdFB2gRVbzflhrNX8DLcz/E8JvMIauHwrloweVikP8POgdFPa9JFzfCqk1KbrXvzxzEsXaQkG7x42VDG9QzSlm3Xd/TmMj3jegaxNSHXIpb2+U3jyC6p1NbSaQghhDbiOBswGzjNSfc1z5kK9XHj4hERLP09ltzSSsJ83ckoqsDFSZBqJ3PPqx1camcDOx+aiZRKpuaao5kkZpfyxa5TfLHrFG8uGcGUPiH4ebrYJK5IaVtV46rx3SmuqOHWqe0f+20KZs+EvRhvR9HhMajOhH4o7K2OoKznLQAOy56sOpJBcm4ZS8Z257lFjkv9GEAXX7OCUurhmfly92l83Jy5aHhXm8mt987tx4ur4rhX9fX3CvG2iQc0VOrpbOeLm8ez+mhms+ZUmSdFh/l5aIszZhZVcsXoSOIyixGiPu7n4eKkxVCHdjMMKrDMnrVOFLjzi33MGxTG/64ZRaHatrdOiebtTUoxZP3IfuO903BzduKfDqYDdCaGdVDGnj0MBeWAoCYua67n1k/2AB1Ts+pso4uP8uD7ujtbzCPaf7qA/mE+NsrpjSUjCPV158NtyVqm2ZkknZyNDAj3ZUAz6jRCfYbikG5+HEqpL8vk7+VChL+HRSZlkLerlt3Y1Hk4fybsrRqcnFvKIz8eIiY5nz5dvC1KNwWqiTPebs4OJ8Z2Jrr4uJFVXNmpZDUUlAMc+WHdXUyNZpq1ZMmEPxsR/h4sGRvJ6B6BLBgSTk5JJe9sVrK99BM5l18zCm83Z82S1adBdyZfeWfltmm9WHUkgwh/D4uFGE1C2FRTae+1fs42Xr1iOG+tT7CofRebUax5WWb278LMAaGsVddZMheFbWiJks7E+n9Pa6NE/pZjmEnN4LUrhvO51eKCo3sEWMy/AfjrhKh2lOrsxGQSPLdoKJeO6oaHqxMPnz9Qm+yaoSsqOmdQmIXbroduxdE/2wiqJdw3rz9r75kGoCVMmLEuCTQ6qsklL/+U9Ajy4oXLhvHYhbZp8AAVNbUW1fOn9QvBx82Zxy60rfreGfFyc+50RoqhoBrgslFK5s4d03tz9fjuTO/XhRFWyQ7XToyyKMET7ufeYCVvA8dMVUvNTGhgIuQ9c+qX8rbucA0aRt9ed83qg79V9uOjFwziomFd+eTGzlHmprMyb7D9grPxmSUIIbhjem/GRAUQ7ufBoSfmcnErFJ79s9K51GUn46W/DNPKx9hj/6Oz8fd0pU5KbUG1h3VFXQ2aR4S/B9/8bYLDZcUBLhwazqbj2aw6kmG4UpuJPj3dzdnJIlb60IL+uDqbtMUvDRwT7udB8tLzqa6t44218ew/XcDm+Bxt+ZJ/z+3XyBUMmoqhoM4AswU6f3A4W0blcMeM3p0qwHg2MsZBpQYzQohGDQcD+1gn7ywZ253aOsmlI7sZo9EW4OJk4p45/ZBSMvbZtdwzu29Hi3TOYSioFtC7izdTdEVWXZ1NvGh0mAadHOs0aXcXpw6px3iuYV4iw6D1MRRUC2iPGloGBm3B5N7BDIo4u+s5Gvx5MBSUgcGfiIYWuTQw6GwIe5WczzaEEMVA01cJ61iCgZyOFqIZnE3ynk2ywtkl79kkK5xd8p5NsgL0k1K27TohKufKCCpOt1x8p0YIEXO2yApnl7xnk6xwdsl7NskKZ5e8Z5OsoMjbXp9lzIMyMDAwMOiUGArKwMDAwKBTcq4oqOUdLUAzOJtkhbNL3rNJVji75D2bZIWzS96zSVZoR3nPiSQJAwMDA4Nzj3NlBGVgYGBgcI5hKCgDAwMDg86JlLLd/4BIYD1wFDgC/FPdHgisBuLV/wHq9v7AdqAS+LfuOu7ALuCAep0nGvjMa9XrxgPX6rY/A5wGSjqzrIAPsF/3lwO81lby6q7nBOwDfu2sbXumsnZE2wLJwCH182IakHceyhy/BOAB3fY71G0SCO7ksm7WtW0a8GMby+sPfAvEAseACZ24bc9U1nZrW6Afls9JEXBXa7atzXUaO6At/oBwYKSuczgODAReMH8Z4AHgefV1F2AMSoenbzABeKuvXYCdwHg7nxcIJKn/A9TX5h9jvCqPo06008hqddweYEpbyau73t3A5zjo9DtD27aGrO3dtiidfoMPKIrCTQSiAVcU42agum8EEOXoOp1JVqvjvgP+2sbyfgTcpL52Bfw7cduekazt3bZWMmUAPVqzba3/OsTFJ6VMl1LuVV8Xo1gOEcBClB8M9f/F6jFZUsrdQLXVdaSU0rzGsov6J+185FxgtZQyT0qZj2ItzFOvsUNKmW7nnE4nqxkhRF+Um2hzW8mrfk434HzgXbuN04i87dW2rSGr7jrt0rZNZCyQIKVMklJWAV+qn4WUcp+UMtnRiZ1JVjNCCF9gBvBjW8krhPADpgDvqcdVSSkLsKXD27Y1ZNVdq83b1oqZQKKU8mRz5G2sba3p8BiUECIKRavuBEJ1HVoGENqE852EEPuBLJTOZ6edwyJQXE1mUtRtZ6usi4GvpGqStJW8wGvAfUBDa9x3irZtRVnbq20l8IcQYo8Q4pYzkLdROpGsFwNrpZRFbShvTyAb+EAIsU8I8a4Qwt4aOJ2hbVtT1vZoWz2LgS8c7GuVtoUOVlBCCG+UYeld1g2rdhANdhLqcbVSyuFAN2CsEGLwn0DWhm6OVpFXCHEBkCWl3NNCGZtMJ5O1zdtWZbKUciQwH7hdCDGlhfI2SCeTdQlt37bOwEhgmZRyBFCK4r5qdTqZrO3RtubruAIXAd+0TNSm02EKSgjhgtJYn0kpv1c3ZwohwtX94SgjjSahDo3XA/OEEOOEEPvVv4uAVJRAoZlu6razTlYhxDDAuaHOuJXknQRcJIRIRhmizxBCfNpJ27ZVZG3HtkVKmar+zwJ+QDFYInXy/q0xeRujM8kqhAhGcf2saGN5U4AUnXfiW2BkJ23bVpG1HdvWzHxgr5QyUz23VdtWT4coKCGEQPG7HpNSvqLb9TNKlhXq/58auU6IEMJffe0BzAZipZQ7pZTD1b+fgVXAHCFEgBAiAJijbjsbZW3QUmoteaWUD0opu0kpo1BGFeuklFd3xrZtRVnbpW2FEF5CCB/za1WOw1LK0zp5/wfsBvoIIXqqVuti9bMapRPKehlK8kpFW8orpcwATgshzOuuzwSOdsa2bUVZ26VtdVg8J63ZtjbIRrIo2uIPmIwynDxIfcriAiAIWIuS9rgGCFSPD0OxNoqAAvW1LzAUJa34IHAYeLSBz7wBJb0xAbhet/0F9Xp16v/HO6us6r4koH9bt63VNafRcOp2h7Zta8janm2Lkt10gPopBw838JkLULKuEvXHAf9Qr1eDkl78bmeVVd23AZjXHvctMByIUa/1I3YyNTtD27aGrB3Qtl5ALuDXSB/fora1/jNKHRkYGBgYdEo6PIvPwMDAwMDAHoaCMjAwMDDolBgKysDAwMCgU2IoKAMDAwODTomhoAwMDAwMOiWGgjIwMDAw6JQYCsrAwMDAoFNiKCgDAwMDg06JoaAMDAwMDDolhoIyMDAwMOiUGArKwMDAwKBT4tzRArQGwcHBMioqqqPFOCeorq2jtk7i7uLkcL+Lk2HXGBj8WdmzZ0+OlDKkPT7rnFBQUVFRxMTEdLQY5wRzX91EXGYxax6cSZifu8W+5ZsSefa3WAI8Xdhy/wy83M6J28fAwKAZCCHsLfPeJhimsIFGSWUNcZnFAOw9lW+zf0tCLgD5ZdVsTchpV9kMDAz+fBgKqpMipeTlP+KISc5rt888kV2qvT6dV2axr6iimn0n81kwJEw5NqcUgz8XWxNyOK4aMAZti5SS7/akkF1c2dGidCjnhIKSQE7JufNDnsot40BKIW+uS+DWTxyuPt7qJGaX1MuQV0ZaQTmzXtnI3lP5/LQ/jeLKGq6f1BN/TxeScw0Fdab8diidfXZGqp2Vq97dyZxXN3W0GOcsVTV1HE4tBJRn8Z5vDnD753s7WKqO5ZxQUMk5pVpHnlpQTkp+WSNndB6yiis4lFJITW0dALtO5DHlxfVc8+5OAHJLq9pNlsTsEpxMggHhviRll/LT/jQSskpY9H/beHbFMXqFeDG6RwDDI/3ZdDyHalVmg+ZTVye57bO9XPJ/27Rtvx1K57bP9lBYVt2BkjVOjZ3f/ZXVx3n85yMdIE3DVNXU8d6WE+w6kUdnX5z1v+viueDNLRxNK2JbouJOT+5EnoqVhzP4bk9Ku37mOaGgXJ1NJGSVUFxRzaSl65j8/Hp2JOV2tFiNcjyzmLHPrOXC/27hi92nAdh4PAuA4soa7bjVRzObfM2yqhrWx2W1SJ6ErBJ6BHoyPjqQPafy2a1zL5ZX13LRsAiEEFw1rgepBeWsPdayz/mzciStkNmvbGTZhkR2nKi/P+/79gDVtXU88N1BfjuUwa+H0jpQSvuUV9Vqr61HzzW1dbyxNp4PtyWTkNXxLsDiimoGPbqSZRsS+XF/Kk/9epTL397OJzvaLbbfIvanKKOnvafyOZautGNnMgKf+e0oH25LbtfPbLGCEkIsauivCef3E0Ls1/0VCSHuEkI8LoRI1W1f0Ni13JxNFJZXc0gdHoNijVpTWye5/9uDfNTOjeyILfH1iQbrjilK6EhakbbtHzP7APD0iqNNvuY9Xx/g+g92k6Rz1zWVxOwSokO86RfqQ1VNHetiLRXQ2J6BAEzrF4KnqxNbErK1fRXVtRxOLURKedb5zdMLy9s86aO8qpar3t1JfFYJz6+M5cp3dmr7vo5Joc/Dv1NUoRgl+ligPapq6tp9NFBQXj+SN3eeZpJz6z0Wd321v91kSsou4Z1NSTZtsT0xl9KqWp5fGcsfR+qNu5/3dz7Fr6e6RlFG8ZnFpBWUA0pCUn5pFfGZxWzT3aNxGcXtmqiUVVTB6bxyLhkR0W6fCWc2grpQ/bsReA+4Sv17F7ihsZOllHFSyuFSyuHAKKAM+EHd/ap5n5Tyt8au5azOyzlwul5BVVTX2hz368E0voo5zZO/Nr3DP1M+2pbMoZRCi20FZVV8vzdFk6N/mA/r47L5cV8qcRnFnNcnmJ/vmMTds/vSPdATfw8Xh9cvLK93B9XWSX4/nAHA/tMFTZbx4+3JjH56NcczSxjazY/uQZ7avgfn92frAzN44dKhjI9WFJSLk4mJvYJZdSSTyhqlne/55gAXvLmFaz/YzZhn1vD4z0c6vUvFzIPfH+Kqd3fydcxpu+6r1uCB7w9SYMd198F1Y4jw99De9+7izck8xy7q4opq+j7yO+9sTmoTOR2RX1ove1yGpYIyJ05E+HtwOLWIU7lt62I/ll7E8ytj+ddX+3nmt2MWRh1gYaiuOZbJrAGh3HxeT2JO5nMsvcj6cp2GU+rvfjyzhLSCclycBKAYjrNf3cSV7+7UkqZu/3wvV727s92SVg6nKW06pJtfu3yemRYrKCnl9VLK6wEXYKCU8lIp5aXAIHVbc5gJJEopWzQGdzEpP+TBFKVT7hbgoVl1q45k8Mn2ZACWbUgElI7cOkutLaioruWxn49w4X+3WGxftjGRu78+oL1feulQAF5cFUd6YQWTegcztJs/AJP7BGs3rjUrD6cz7Ik/iHpgBXEZxRxIqVdK9tLE7VFdW8fzv8eSU6JYyHMHhREV5KXtv3ZiFBH+Hlw+JhIhhG57D7KLK/nlQDqHUwtZcVAZsW46royqPtyWzOHUztUZJGQVsz7W1i25IU6R+b5vD/LFrlNt8tnmTnTXQzO1bev/PY3p/buw9YEZXDuhB7dP70XPYC9ONpCAkpKvWNZvrU9sEzkdUVBWP4KKzbD8XfedysfVycSnN41DCPhpf2qbynLvtwdYtiGRA6rh98vBNJ785Sgfb08G4HBqIdHB9ffwbdN7sWRsdwD+/mn7JR01h5LKGlLVUVNCtqKgJvcOBuCznfX35O+HMygoqyIhS/GQ/LCvbdvazOHUIoSAAeG+7fJ5ZlojBhUppdT70zKB7s28xmLgC937O4QQB4UQ7wshAuydIIS4RQgRI4SIKS5UOuaDKYWYBIyJCtQe8r99uof//HSEj7YlE5tRzOIxkUDbP0SAdhPpqaqp4+2NivW7cHhXNt83neGR/twzu692g87s30U7vnugJ/ll1RRX2Frfa3QxoLmvbdLcGQPCfYlJtlRQuSWVfL83haoayxFCYnYJpVW1XDcxio9uGEu/MB/C/dzxdXfG193ZYUWJyb2DCfZ2Y1tijk1bvnrFMAD2nGy/FHlr6uokr6w+bpGZOOuVTVz/4W6L0XWemoTSK0Tp0L7Yddru6PtM2HMyj4SsEh45fwBdfN2Z2CsIgMiA+pHTEwsHc+/c/kQFeXIyt4y6Ovujz1RVQUkp2XQ8m9s+28Pm+Gy7x7Ym+eror2+oN8fSiy2U6KbjOYyOCqBnsBdDIvz4cFtyq8ROdiblMv/1zRbfr6a2jiQrF+jP+9N4f+sJHv3pCKfzyjiUWsSI7gF8dtM4HpzfnxGR/kSHeDO2ZyDJuWVkFVWcsWytRUp+Gc/9dox7v1EM1gnRQWQXV1JaVcu46CBcnISmhCIDPYhJzuNznRHVkDFzJpRX1Vo8v4dTC+kZ7IV3O0/Obw0FtVYIsUoIcZ0Q4jpgBbCmqScLIVyBi4Bv1E3LgF7AcCAdeNneeVLK5VLK0VLK0aEhiqWRWlBOiI8bvUK8yCyqJL+0CrOX6TE1w+i8PiH0DfUm5mTbp/fqFZRZMZhdb0vGduf1xSOIDFTcaecPDQfAx82ZPqE+2nnd1f0n7bhN4q0U4P82JtIv1IcLhoYTm6F0IlsTcpBS8vraeO7++gD/XRevHV9bJ7Xsx+smRjG1r1K9RAjBun9PY9uDM3GEEAIPVxPf703lvS0nmBAdxPGn53PiuQVcPDwCL1cni9hEeyKl5NoPdvHG2njtwTe7IgEtlRfQfP3/ntOPVy4fxtH0It7bcqJV5flubyo+bs5cOU6x296/bgyb75uuuab19AjyorKmjszi+k70WHoRb61PoK5OakaMq7OJOz7fy2+HMnh+ZWyrymuPfHUENSE6iNSCcqa+uIH1sVlkFlUQl1nMFPXemdY3hNzSqma5mO1RUFbFTR/FcCy9iOWb6t2Zh9OKKKuq5apx3ZnSN4RnLhlMemF9Wy1dGUtOSSVDInyZ1DuYW6f20kb+/5rVF4BjGR2fyGFmygvreXtTEr8fzsDbzZnrJkVp+7oHetJD9Wb0CPJk4bAIDqQU8sLKOJxNgql9Qxx6V86Uh384xKXLtmuj5cOphQzu2r7uPWgFBSWlvAN4Gxim/i2XUt7ZjEvMB/ZKKTPV62VKKWullHXAO8DYxi4ghOLWAwjzdWdQhNKQH21Ptjm2e6Ano3oEsudkvkMrtbXQPzhZaodjTl64bVovi2OjQ7x5auEgll09ymJ731BvAP79zQGL7akF5RxMKeCuWX24b14/bfu8wWH0UxXc1Bc3cNW7O9l3uoA1aibgyiMZ2rE7T+RyMreMgeG+ROlcIgDB3m6NWksLhykB0zoJY6ICcHU2IYRACEGPoIZdVW1Jdkklm9UElOOZJTyz4ij9Hlmp7f9SzZgEyFSt6VA/dxaN7Eb/MJ9WzwBNLygnMtATT1elPd1dnDTDxJoeavxv9dFM1sVmUlFdywVvbuHFVXGc/+YWTUHllFRRVFGDEBCfWUJtG9/LZhffuOggbdv1H+5m3LNrAZjUSzESrx7fA8Am7tpcVh7OoLiyhu6BnhaG3k71t/nnrD58fMNYloypd9YMCPfVXM1DI/1trtk31BuTgBUH0ziWXtThGXJ1dRL9z9a7izd9unhr77v6e2jPf/dAT2YMqPesvHz5MHqoo+3WjvUeSy/ie3XUtjUhl/zSKtIKKxjUtX3de9BKtfiklN8D37fw9CXo3HtCiHCdy/AS4HBTLjK4qx8p+UpHMLJ7AELAa2uU0cKXt4xn8fIdAEQFezKyuz9f7DpFUk4Jvbv4NHTZM0Kf+ZRRWEG3AE+SckpxdTbRVRcYN3PNhCibbdHB3vh5uBCbUUxlTS1uzorLbdPxbKSEi4Z1JTrEm7iMYmLTi7nxvJ5aNpCZpb/HklZYwcBwX46mF/H4z0foFuCBm+q+e/fa0S36fv+e2w8PVydeXBXHQCvrqmewF0fbMSBdU1vH1zEpzB0UqrmAzN/3nc3KiGjRiAiq66RF9mSGqqDCfJW6g4O6+jXJZXY0rQg3FxO9QrwbPTazqNKmrqEjzPG/R39SRvx9unhryudYepFFQgXAzP6hrDmWSUp+mWZttwX5ZdV4ujoxtW8IV4yOpKSyhhW6TNkB4cpz1MXXna5+7myKz+aGyT1trlNYXk1Sdgkjult67qWUHEwpJDrECx93FzbH5xDq68b1k6J44pejpBeW4+3mzO+HM4gO8aKLj9KeJpPgenXU4ebsxLH0ItxdTAzrZquggrzdmNI3hK9jUvg6JoU7Z/Tm9um9ARy6stuSdPXeu2N6b77cfZobJ/e0MFy6+rnTW72/zP3arodmEuzthskkyC6upLiihsLyavw9XVtNrvmvb9Ze7zuVz3BV2fcJbfxeb23OeASlppXHCyEK1VTxYiFEk3omIYQXMBtL5faCEOKQEOIgMB34V1OuNby70ohzBoXh5+GidTiTegcxPjqIA4/NYceDM/Fxd2GEeuzeU2fmhmiMAl3mk3k0lZRdQs8gL5xMwtFpFphMgscvGggo5Yeyiyu57bM97E7Ow83ZpHVory8ewap/TcHX3YUgbze+uHk8n9w4lpn9u7DrhOJLfv+6MUSHePHhtmSeXnGM//yo6P7wJnae9rh9em+OPDGXeYPDLLb3CPLkRE4pj/50WIsDJWWXWATbW5PX1sTz0A+H+DomRYsr3TmjN85qO7/8l2G8csVwBnf1JaOoQpMjs7ACISDExw2AfmHeZBVXNihnZlEFC97YzCVvbW1SvCqzqIJQ36a1sfVvEZ9VQnSIFy9epiTSbE3I4bw+wdw5Q+lYb5zcE5OA5ZuSKCyr5vL/bW+TOYAZhcp38HJz5vnLhvLmkhHcM7svzy0awtvXjLJwV84fEs6GuGx+PWib1n3P1we45P+22ZTK2hSfw8K3tnLvNweRUrI1MYfJvUMYqSqyfacKuO2zvew/XcA4dbqDmccuHMRjFw5ihhq7XTSym8Pn69qJUdrr3w6lc/nb25n96kYLFzDQ5t4VgJNqG0zsFcTuh2dy4bCuFisFBHu7MX9IOBN7BXGdKncXX3dM6ndz5P6XUpKSX8aD3x/iaJptV3wip5Svd5+2+c4A+VaFAVYfzdQyBbsHtp0B5IjWGEG9AFwopTzW3BOllKVAkNW2a1oixM3nRTMg3JcpfRRXQxdfd9ILK3hw/gAA/Dxc8FPTtXsGe+Ph4qSlnBaWVXPtB7u4fXpvZg8MbcnH26WghqgJ7QAAIABJREFUvIpwP0WOHUm5FJZXE5dZ3GxfbnSwYrkkZpey92QKvx1S3HT9w3y0m9WaCWog/lReGWvVzLUwP3f+Nasvd36xz+JYfXZeS7BX1XxMz0DYkMjH20+yIS6b/109igVvbGZEd39+uG3SGX2ePcxZi6fySvH1UOQZ0T2AhGctp9GZRxkp+eX4e7qSmFNKtwAPrWPoq7pHj6QVMUnNojJTU1uHEELLqiqqqGFHUi7T+nXBEZU1teSWVmkGU2M4O5m4dUo0KQXlDAz35cVVcbxw6VAqqpVRcXl1LZN7B3PLlGj+MbMPLk4mLh4RwS8H0jivTwi7kvNYvHwH3/19AqN6BDbyaQ2zPTGXgykF3DIlmlN5ZRbWvckkuFOdp2fNBUPDeW/LCV7+4zgXDO2qbS+prGGNOt9v14lceurcytvVygmrjmaQmF1KQVk1w7v7MyDcFzdnE3tO5mtuW3NGnjVjeway5u4pFlmo1kzv14XtD87g690pvLrmuLb9jbXx3Du3v3L95TvYnpTLq1cM45IR3RpsozPBHKONCvayeAbvn9efsqoaTGpVl89vHm/3/Ag1rJFWUM6wSH+klDzxy1E2Hs9mRHd/vt+bysa4LDbfP0NT2KWVNcx/fRMV1XWUVNbYjHJj1ficq5OJO2f05uXVx1l5OAMhlCSN9qY1FFRmS5RTa+OkBg3NvLl4BJvisxkcYasMnEyCfmE+fLA1mehgL/aeKmD/6QJu/jiG5KXnt5pM+WXVRAZ6UlhebZEquqiZN32fUG+cTYL9pwu0NGOAgU3wCV84rCu/HkjnoQUDtPdT+4Xw/Z4Uvtubyt2z+zZLlqZi7gje2XSC97ee4JZPlOVQ9p0qoLiiGh/35s5EcIyUUjM2ErNL6RagdKT+nraf0cVXGSllq7Ubk3NKLdx0o3oE4GwSbEnIsVFQ01/egK+7C7V1kqHd/DiSVsTu5DyHCiq1oFyrwNA9qOkP94PqbwVoLih9hYbJfYIRQmjzZOYOCuP7val8trN+lsa2hNwzVlBL3lHc4jP6d+F0fhlDmzgHZkT3AJ64aBCP/XyE45nFmtLfpauecTClkCvGKMH31Ucz2ZaoKB8p0SbS9wrxwtXZxJAIP75VS+w8c8lgbQqGPZrisg/382DRyAgLBbX2WBZXj++Bk0mwXR2BPv3rMWYPDGuzzLWTuYq739p4+btVfNoRZndvakE5q49m8vIfcZqCMY9Q0worSMou0RKvvok5rRk7m+24YePUpIjN90/HxcnEso2JbDyeTXSwlxZeaE9aI4svRgjxlRBiSXMqSbQ13YM8tYCtPcxzDP7z0xGLuQRlVTWkF5az8nCGo1ObTGFZNQGeLjY34AVqxl5T8XR1ZnikP9sSc9mVnMfUviFKB3DBoEbP9XV34YtbxltMsPN1d+G6ST355c7JTO/v2Po/U8L9PDTXREp+OV1UN9rpvPIGzmo+GUUVWhr0ydxSMgor8Pd0sRtXMMuQXqC4XHNLqgjxdtP2+7i7MLJHgDafy0x6YTmn88o5klZEbEYxU/qEMLirL78fzmDea5t46tejWrB6W2IOUQ+sYNLSdcx6ZSNQPzJrKWF+9QpuQJilYXJen2BcnU1sjs/B280ZPw8XXllznLmvbiK+hRM59S6uR348TIFqbDWV+arL1zxigvq5NMMi/TmYolQdufWTPby+Np6DKYXcOjUaJ5PgR/V57K0mDAwI96WwvBonk2B8dJDth7WAyEBPbVRx0+SexGYUM+G5dcx8Sfm97pndl9zSKj7Z3vrlkU7klDLj5Q28vSmJyAAPh16QxvDzcKGLjxtPrzjGzR/HaMrJ7P41P3uxuqzFnSfyiPD34NYp0Ww8nm3jyo7LLCbAU7luoJer5rof10rt3lxaQ0H5olSBmEN9dYkLWuG6bcrt03uzZGwkQV6uBHi6MFKNS729MYkJz63jb5/usajS0FwqqmtJLSgnyNsNX9W1eOeM3sQ/M98ijbypTOgVxIHTBWQXVzJzQBeunRiFn50RQmeje5Anwd5KAPeiYYq7p7WL+Zr97OY5JKkF5Q5dal39PPDzcGFLQjZSSvJKqwj0tgwwT+0bwpG0Iq1kU0V1Le9sUhItzJ3a+OggxvcKIim7lNiMYt7bcoLtSbk8/MMhizJGAEMi/Bh0him6eiveukPzdHXmb1MVq7uksobiimqkVDqbXw/alvxqCuZ738kk2KnGMCMDmq6guvi6MzDcl41x9YrePJdmWt8QDqcVsj0pl9SCcoZH+vPI+QP416y+RAV5UlxZQ7C3m2Y4mBXVFWMim5SU0lTW3zONt68ZZaH0iitr6NPFm6vH96B3F2/2n2796ShP/nJES+SZNaDlIQUhhGaE9wrxYv7gMF5fPJx75vTj0ONzeGB+f5xMQqv8UVsn2ZaYy8ReQcwcEEqdhBWH0nnljziueW8nCVklxGUoI16zy/GGST0Z0d2fq8c3d2pr63DGY1e1mkSLEUIkA8VALVAjpRwthAgEvgKigGTgcillq94pHq5OPLdoKI9fVEtNraRWSoY+/gevr62fJxSXUazVn2suR9IKKamsYUqfEEZE+nM0rYgFQ8JbvFz6hF5BvLkuAcCu27Iz88udkzmRU8rQbv68v/UEh9OKmDMorPETm4i5YsWUviFsT8rlSFohAx3MeDeZBJeMiOCznSc5lVdGVW2dlhFmZkqfEF5cFceWhGzmDw5n6ON/UFVbx6Uju/Gv2X3YlqA85N0CPLRJ1wA7kvI0V27/MB/Ncn32kiGt8j1dnUzgwNi+e3ZfpJQMjvBj2YZEbR5SS9btSswuYf5rSibX5N7BbFRHk82NQUztF8I7m5IorqjG282ZgymFjO0ZyFXju/P62nhNkT964UAtGWJAuC+J2aUM6+andZJXjInE3cVkEc9qDboHedI9yJOSyhoWDAljZv9Q3F2cmDc4DCeTIDrYi8RG6iI2l/KqWrYk5ODr7syV43pw79x+jZ/UAHdM702YrzsTegVZjHDNLvToYC/tPjyUWkhheTXn9Q1heKQ/gV6uPPxDfZL0Pd8cILekkjFR9X3e4Ai/NokZN5UzVlBCiL4ok2tDpZSDhRBDgYuklE834zLTpZT6yocPAGullEuFEA+o7+8/U1nt4ebshNk4XTwm0mKOTGxGUYsV1FZ19dlhkX6qz9txZlFT0PvdHXW+nZVwPw/CVRdVvzBfizWQ1sdl8dKqOL752wRtnlBTqK6tY/mmJBaPiWR7Ug4Dw321oHtmUSUz+jvuTC8YGs6H25L5VK1ura/oADCoqy9BXq58vzeV6hpJVW0dU/qG8PylQ3B2MnH5GKUjiAr24v55/YkM/P/23jxOrqrM/38/VV297+lO0uksnY2EhJCFJATBDJsQQCEqKigOIzroKIMsw3xhXBAcvqKMDP5m5qcvxAUHRFYDghqQTUAIZF8gIfvSSSfpdDqd3rfn+8e9VX27urau9VZy3q9Xvarq1KlzP/XUvefcc85zzlPAnc9u4g/rLK+1+648nSvPGEt9cwdvbm3ktNrk/F+rvnMhkXzLbr3Iquw+MnkEuxrb+fcXLPfs4fLEe3vpttcIfWL2mIEGahg9KLAat5++tp01e5opyPXS0NLJ4lOqGVmSz/nTR/LK5kNUFecOWl/zsRmjeH79gUHzf/k+L59bkLo7+OK8HP7/L5wxJH1SdTGvbD5ES2cPhT5vyIXVw6W+uYOePuW+K09jaRI2XvV4hM/au+OEYtroksDNyutbDiNi/S+5OR6+uGgCP3l5K/d/djbv7DjCnzc20K+h524zRTJm/34O3Ia1WBdVXS8ivwWG00AFcwVwrv36YeA1UtRAOTljQgW/e28vU0cWs6epPe79+l56/yD3v/Qhs2rLAhVzIo0TWBfRRTNGsWjSiIys2UgWZ0woZ9ma/fT1K16P8NXfrKK7r5+1e5v5yOTBTgnt3b0U+LwhvQz/tLGB+5ZvYW9TO6t2H+W6sycyqnRgLml8hPmSueMrGFGUy6/e2gUwZG7F4xG+8tFJ/PDPm3ljayMzx5Ty8JcWhNThn9D+9Vu7AruTnD99JCLC2IpCrgrjcRYPsTqWlOT7mDW2jJqyfN7bNfyBh7dsh4Ubz5/CRyaPINfroaY8f9gV17TR1lD2hwePs/1wK4W53sDc1P98fh7v7mpiysjiQZPvl88ewymjSpg+OnXrE2NlQV0FP3vdGlkZV1nAq7eeG1cj1drVS36OhxyvJ7BzSazr4hJlVm0Zz68/wDceXc0LGw5w1qQRVBZZQ9o3XjCVc6ZWsaCukqa2bp5YaTmiVCRxTVWiJGMOqlBV3w1K6w2ZMzQKvCgiq0TkejttlGOxbgMwZKDWuRff4cPJ2YvM7y49YUQho0rzaWgZftiIvn7lH39jeawFL6pMlAf/fn7IxY/ZxNxxFbR29QZ2B8jNGboTPcBf3j/IjO8u5zvPhl6nfcC+0J9evY+ePuWcqVWD1hpNGRl+rsLrEc6fPpJe2xFgbEWoRdMTOHvKCC48dST/8ZnZUV3x/R6kH51axQiH00UmqSkv4GBLJ/39yuaGFlrs/Rz7+pV/f/59lm8a6gjU369sP9TGdWdP5JaLpjGmvIC37zifV249d9jLEaqK85hYVcTrHx7mxU0HueDUUYFrrMBe9Bt8jYhYrtWJLn1IBudNGxmYP93b1BGYi4uV7t5+Lv/vNzntzuV87sF3+Jcn1/Hfr1rD9Ik6zcTKVQvHU5KXE1hU/a3LBjxEvR4JDOc5b9KqXHL+QnIaqEYRmYzV0CAiV2LtoRcr56jqPKwtj74hIoudH6rlGjVkZMO5F191dXXwx3Exv66CXK+Hr/7dZEaX5ge2wRkOzlhIZ0+tipDz5MS/oPqmx9fS09cfWCy4zrF3W3+/8hW7kX/knT2oKkse+Cv3vzTgFuy3c0+fdWosqKsMLLaFgc1fw+Fc7xaqZ1Kcl8OjX1nEQ9cuiGkH5386dzKPfPlM/vvz86LmTRc1Zfn09isrdjax5IE3+MajVvjwNXuO8tCbO7n1iXVDvnPweCcdPX1MdNhvRHFe3CMAF0wfyRtbGznS1s1ls5I375gOPB7hZ9ecEVgAPJz9BY+0dnHKt//EenvLp1W7j/LUqn28u7OJj0we6MWkmrICH09//SPc88nTeOeOC8LOX09whNipKU9P7y4WktFAfQNreG+6iNQDNwFfi/XLqlpvPx/Cige1EDgoIjVgbX0EpCV068iSfD685xIW1FUyqiy+Bsr/nTs/MYOrI4wNn6xMHFHEmLJ8PjjQwm9X7Ak0MKv3HA24aT9nz+WU5Ofgtdd/bW44zv/ncGDx70kH1m4h+T4vPq+H0vwcCnO9Ubf9+ehU66ambsTw5lXCkeP1cM7UqsBicDfgH15+ZrU1dPPG1kY6uvsCk+atXb1D9nHzO1VMTNK2SUvn1uL1CDPHlPKxGdnVQAHMr6vkl/+wgOqSvCG7qEci3NCqR+A7H5+RLHkxccqoEr5w5oSIw4rO+cVEdpZJNgnNQYmIF/i6ql5ob1vkUdWYF144v2O/vgi4G3gOuBa4135+NhGd8TC6NI8Xj3WiqoHhBlWlt18jeuL5d/89e0pVUiZVTzQ8HuGv/3oes+96kaftinNhXSXv7mriaHsP5QU+vveHTcwbX85n5o/jjmc28LPXB2If1Td3BBq4+RMq+OJZEwLu6wAv3PhRRKLP+RXkennp5sUJzw26GX9F41zTt35f86C1US0dvZQV+mhq6+abv1sT2HdtTJLuok+rLWP7/40aFNv1zJ9QMawItrscGyW/cOM5/O/bu7nj0lNp6RjeerJ04dwNpqY0/TtGhCORkO85qtoHnAPWtkXDaZxsRgFvisg64F3gBVX9M1bD9DER2QpcaL9PKyNL8unq7Q+E4Qb458fWcP6PX4sYdfWVzYeoLS8YtCuxYTA5Xg8zx5QFhj8usHdprj/awa4j1jY3Vy0YH/DuWr7pIHn2XNWbWw9z6HgXu460c8msGq6YUztovmJcZWFgJ4loTB1VwqQkrqtxG/4G6nhXb8DpYOuh1kEhGvbbXn73vPABb2xtDCxlGBnj1kwnC2dMqKChpZMjreHnpR9/bw+f+K83aWrrZldjGwU+6yZo5pgy7v306ZQV+FzZOPmZZQ//+bcKcwOJKHkXmAesEZHnsOI5BW4b7B3OI6KqO7BCdASnH8GKspsx/FviHGzppKzAR2dPX2DR4/r6Y4F1G8FsPWSFTXfDJK+bmVlbyrt2+Gr/voH1ze2B3umUUcWDFmVednoNb21r5K8fNjLd3klhgosvdjfgnOe45LQa9jS1s/1wK4dbu6go9HG0vYf9zR2cWlM6KBR6Ya437YHp3I7fI3FLw3E+MiW0E8ETK/exof4Yv3prJ1sPtTKrtiyuRfmZ4pf/sACRxPfmTCbJGIPKB44A52PtIJEVO0lEw7/yf5XtOuzfKwxgkyPg3dG27kAAPFWl/mhHSK8ww2D+6dzJzKot44uLJgRcwvcd7QjMgUyqKqIoL8danIo1jv7RqdW8ua0xECJjZKl7vI3ciIgEGpoFEyuYVG0tPD3U0sVseyhvf3MHPX39g2IuucnN2C2cNqaMXK+H/3hxS8hdwLt6+9i036oHfrtiDx8caIlpr0w3UV2S5yoPPkisgRopIrdgxWvaYD9vsp9jiuHkZiZXF1GanxNofPzb6eR6PWxybGG/4J6/8PH/epPOnj4aW7vp6u1Punv5icjIknyeu+Fs7r5iZmCn+R2NbexobKOi0BeIb/OpedZixotmjOLMiZUc6+hhxQ6r5+X02jOE5uHrFvBP507mzIkjGF9ZyJ4jbTS2djGjphSfV9h/rJOdjW109/UHPBvbu4ezSuTkoKIol+8vncnqPc1M+/afh9ho95F2Onv6mTOunCNt3bR392Xdji9uJJF+vBcoJvTmK6kPppJiRITpNaU8umJPYPua8ZWFjK8sZOP+Y3T29HH/Sx8G1tKs33cssKandpgr7k9WnEMJs8eV8/h7e6kpyx8UhuGuK2Zyy0WnMLIkP+Dx98pmawNSt93tuZEzJlQGdjUfV1EYCNUyuiyf0WX57G/uCAzvXb94EnuOtPPlLF9rlyo+PW8s/+fpDQD8aUMDnz5jICqBP4LwJ+fWBtzRk7WDyMlMIj2oA6p6t6reFeJxd7Qvi8g4EXlVRN4XkU0i8k07/XsiUi8ia+1HxlyATg1azX7FnDHMrC1l0/4WLv3JGzz414F92N7efiSwCaoZ4hs+t3zsFPr6lX1HO5hYNTD3lJfjDeyVN2WkFV1415F2RhTlxr2v4cnKWMec3ciSPMZVWOHUNzccx+cVZo8tZ/nNiyNunXMyk+P18NfbziPX6+GVLYNXvryx9TBVxbmDti+acgI74KSLRK7wRGfSeoFbVXUGsAhrka5/gcB/quoc+/HHBI8TN7Ps/e9Oqy1l010Xc+tF05g5pgxV2GHPlfzmuoXMHFPK37Y3BiJb1poGatjMGVce2P1hYlXoHqjXIyydY7mUV6RpoeOJxDTHhH11SR6LJo1g0/4WfvradgQJjAAYwjN+RCGfP3M8yzc28Na2xsA6stV7mlk4sZKyAh/nTatm6ZwxZplJEkjEggl52anqAVVdbb8+DnwAJL57YhK5Ys4YfvkP83nky2cG1gnMsRut2vICdt17GYtPqeacKVWs2NnET1/bzimjiilNYjC+k4nPLxzPmLJ8Lp0VPl7WxfZebi0JhEI5WZk7fmDD4VGl+YM2ZJ1slkXEzNfPnUxlUS5feGgFX390NUfbutnT1B6Yc/rVlxbywFVzM6zyxCDuBkpVh7cxVQREpA6YC/gD6dwgIutF5JciEtKfOxV78QXj83o4f/qowIQ9WHdQv//6R3jhxnMCaZ+Zbw2JtHb18sWz6lKi5WTgunMm8rc7Loi4NmlBXSUXzRjFT0wFMGx8Xk9gPVRteUFg3QvAg18cupu3ITQjS/P5wz+fw2fnj+VPGxuY+/2XAGufSUNykeCtTtIuQKQYeB24R1WfEZFRQCOWo8X3gRpVvS5SGfPnz9eVK1emXmwEzv+P12ju6OFvt5+f1buNG05sjnf20NrVG9gG6bvPbiQvx8O3Lkvv9jsnAu3dvdz42Bre3dnEl8+ZxI0XTHHVGqJUISKrVHV+Wo6VyQZKRHzA88ByVb0/xOd1wPOqelqkctzQQDW1ddPbPzT4ncFgMJxInBQNlFi3Gg8DTap6kyO9xh9qQ0RuBs5U1auilHUc2JJKvUmkCquHmC1kk95s0grZpTebtEJ26c0mrQDTVDUtW2Rkcj+Ts4EvAhtEZK2d9m/A1SIyB2uIbxfw1RjK2pKuFj1RRGRltmiF7NKbTVohu/Rmk1bILr3ZpBUsvek6VsYaKFV9k9Cu6hlzKzcYDAaDezCO+gaDwWBwJSdKA/VgpgUMg2zSCtmlN5u0QnbpzSatkF16s0krpFFvxt3MDQaDwWAIxYnSgzIYDAbDCYZpoAwGg8HgTlQ17Q9gHPAq8D5WDKlv2umVwEvAVvu5wk6fDrwNdAH/4ignHyuy7zq7nLsiHPNau9ytwLWO9HuAvUCrm7UCJcBax6MReCBVeh3leYE1WAumXWnbRLVmwrZYSyg22MdbGUHvEqw1ftuA2x3pN9hpClS5XOsbDtvuB5alWG858BSwGWuPz7NcbNtEtabNtsA0Bl8nLcBNybTtkHKiZUjFA6gB5jkqhw+BGcCP/D8GuB34of16JLAAq8JzGkyAYvu1D2svv0UhjlcJ7LCfK+zX/j9jka0nXCXqGq1B+VYBi1Ol11HeLcBvCVPpu8G2ydCabttiVfoRL1CsBnc7MAnIxbq5mWF/NheoC1eOm7QG5Xsa+PsU630Y+Ir9Ohcod7FtE9KabtsGaWoAJiTTtsGPjAzxafidzK/A+sOwn5faeQ6p6ntAT1A5qqr+WNU++6EhDnkx8JKqNqnqUay7hSV2Ge+ovXOF27X6EZFTsE6iN1Kl1z7OWOAy4KGQxomiN122TYZWRzlpsW2MLAS2qeoOVe0GfmcfC1Vdo6q7wn3RTVr9iEgpcD6wLFV6RaQMWAz8ws7XrarNw9GbLtsmQ6ujrJTbNogLgO2quns4eqPZNpiMz0EF7WQ+ylGhNQCjYvi+196J4hBW5bMiRLZarKEmP/uII7SHi7ReBTyu9i1JqvQCDwD/CvRHyOMK2yZRa7psq8CLIrJKRK5PQG9UXKR1KfCyqrakUO9E4DDwKxFZIyIPiUhRiHxusG0ytabDtk6uAh4L81lSbAsZbqDsncyfxhrHHGRYu4KIWEnY+fpUdQ4wFlgoIhE3lj1BtEY6OZKiV0Q+DhxS1VVxaowZl2lNuW1tzlHVecAlWME6F8epNyIu03o1qbdtDjAP+KmqzgXasIavko7LtKbDtv5ycoHLgSfjkxo7GWug7J3MnwYeVdVn7OSDIlJjf16D1dOICbtr/CqwRETOdISMvxyox5oo9DPWTss6rSIyG8iJVBknSe/ZwOUisguri36+iDziUtsmRWsabYuq1tvPh4DfY92wjHPo/Vo0vdFwk1YRqcIa+nkhxXr3AfscoxNPAfNcatukaE2jbf1cAqxW1YP2d5NqWycZaaBERLDGXT/QwWE2nsPyssJ+fjZKOdUiUm6/LgA+BmxW1RU6EDL+OWA5cJGIVIgVAPEiOy0btUa8U0qWXlW9Q1XHqmodVq/iFVW9xo22TaLWtNhWRIpEpMT/2taxUVX3OvT+DHgPmCoiE+271qvsY0XFhVqvxHJe6UylXlVtAPaKyDQ76QLgfTfaNola02JbB4Ouk2TadggaxYsiFQ/gHKzu5HoGXBYvBUYAL2O5Pf4FqLTzj8a622gBmu3XpcDpWG7F64GNwHcjHPM6LPfGbcCXHOk/ssvrt5+/51at9mc7gOmptm1QmecS2XU7o7ZNhtZ02hbLu2kdA0sOvhXhmJdieV1td+YDbrTL68VyL37IrVrtz14DlqTjvAXmACvtspYRwlPTDbZNhtYM2LYIOAKURanj47Jt8MNsdWQwGAwGV5JxLz6DwWAwGEJhGiiDwWAwuBLTQBkMBoPBlZgGymAwGAyuxDRQBoPBYHAlpoEyGAwGgysxDZTBYDAYXIlpoAwGg8HgSkwDZTAYDAZXYhoog8FgMLiSnHi+JCLzIn2udnAsg8FgMBjiJa69+ETk1Qgfq6qeH7+k4VNVVaV1dXXpPKTBYDCclKxatapRVavTcay4elCqel6yhSRCcVUNjRfexYQRheTlePjwYCvzxpezek8z4ysLKfB52XLwOGdMqGDV7qOMqyyg0JczJK0oN4fNDQNpYysKKM6z0vzl1ZYXUFrg44MDLYG0MWX5lBXm8sGBlsB3a8ryqSjM5X1HvtGl+VQWWWn+fCNL8qgqzhuUr6o4j9FleWysd6blMqo0n037B9Iqi3IZU54/KF95oY+xFQVsrG9h7vhy1uxppqzAx/jKQjbUHwukleTlUFdVNCitOC+HSdVFrN93jDnjylm7t5nCXC9TRhazft9AvgKfl6mjigfly83xcOroEtY58uV6PUyvKWH9vmPMHlfOur3N5HiEmbVlrNvbzJxx5azfZ0W4nlVbxjo736b6Y/SpcvpY6zuzx5Xz/v5j9PRpoJzZY8v4oOE43b39AQ2zx5ax5eBxOnv6B+XbeqiV9u6+QL7Tx5ax/VArbUFpOw+3cbyrN5A2q7aM3UfaaOnsDfymWbVl7D3aTnN7TyDfabWl1B/t4Gh7T+B/mFVbxv7mDo60dQe+e1ptKQ3Humhs7Qrkc6b5880cU8rh410cOj6Qb0ZNKUfaujjYMjitqa2bhpbOQNqpNaU0t3dz4FjnoHzN7d3sd6RNH13C8c5e6ps7AueiMy1avtauXvYdHcg3bVQJ7T297G0ayOdM8+c7ZVQxXb397D7SHsg3dWQx3X1WmjNfT5+ys7ELo/FqAAAgAElEQVQtkG/KyGL6+oem9fcrOxxpk6uLUIUdjW3Mn1DBSn8asONwW+AYk6qLEGD74YF8k6qKELHS/OVNrCoixyNsPdQaSKsbUUiO18O2oLRcu/7xp4WqkyaMKCQ/Z3h1kv+7zjrJn6+2vICS/MH5wtVJ5XZaqDrJnxaqTqouyWNkSR6b9rfAqo+HCvOeEhLezVysqLAzgHx/mqr+JsEyfwn4o6RGjTpbPHaaVl1zf7RsBoPBYEiQ3T/8+CpVnZ+OYyXkJCEidwL/ZT/Ow4r/c3kSdP0aWBJrZp9XknBIg8FgMLiJRL34rsSKAtmgql8CZgNliYpS1b8CTbHm94hpoAwGg+FEI9EGqkNV+4FeESnFimk/Lsp3koKIXC8iK0VkZXd3dzoOaTAYDIY0kmgDtVJEyoGfA6uA1cDbCauKAVV9UFXnq+r83NzcdBzSYDAYDGkkLi8+P6r6dfvlz0Tkz1hx69cnLstgMBgMJzsJNVAAIlILTPCXJSKL7Tkkg8FgMBjiJqEGSkR+CHwOeB/os5MVSKiBEpHHgHOBKhHZB9ypqr9IpEyDwWAwZBeJ9qCWAtNUtSsZYvyo6tXJLM9gMBgM2UeiThI7AF8yhCSCcTI3GAyGE49Ee1DtwFoReRkI9KJU9cYEyzUYDAbDSU6iDdRz9sNgMBgMhqSSqJv5wyKSC5xiJ21R1Z7EZQ0TM8ZnMBgMJxyJevGdCzwM7MJqJsaJyLXGzdxgMBgMiZLoEN+PgYtUdQuAiJwCPAackagwg8FgMJzcJOrF5/M3TgCq+iFJ8uoTkSUiskVEtonI7RHzJuOABoPBYHAVifagVorIQ8Aj9vtrgJUJlomIeIH/AT4G7APeE5HnVPX9RMs2GAwGQ3aQaA/qn7B2kbjRfmyy0xJlIbBNVXeoajfwO+CK8NlNH8pgMBhONBL14usC7gfuF5FKYGySdpWoBfY63u8DznRmEJHrgesBSsZMoigJBzUYDAaDe0g0ou5rIlJqN06rgJ+LyH8mR1pkTLgNg8FgOLFJdIivTFVbgE8Bv1HVM7Ei7CZKPYMDH46100JyvLM3CYc0GAwGg5tItIHKEZEa4LPA80nQ4+c9YKqITLQXAl+F2bHCYDAYTioSbaDuBpZjOTS8JyKTgK2JilLVXuAGu+wPgCdUdVOi5RoMBoMhe0jUSeJJ4EnH+x3ApxMVZZf1R+CPySjLYDAYDNlHolsd5QNfBmYC+f50Vb0uQV0Gg8FgOMlJdIjvf4HRwMXA61jODMcTFWUwGAwGQ6IN1BRV/Q7QpqoPA5cRtF7JYDAYDIZ4SLSB8ofWaBaR04AyYGSCZRoMBoPBkPBefA+KSAXwHSw38GLguwmrMhgMBsNJT0I9KFV9SFWPqurrqjpJVUeq6s8SKVNEPiMim0SkX0TmJ1KWwWAwGLKXRLc6GiUivxCRP9nvZ4jIlxPUtBFrZwoT9NBgMBhOYhKdg/o11mLaMfb7D4GbEilQVT9wxpgyGAwGw8lJog1Ulao+AfRDYAeIvoRVxYCIXC8iK0Uk4fhTBoPBYHAfiTpJtInICEABRGQRcCzal0TkL1jrp4L5lqo+G8uBVfVB4EGAvJqpGrNig8FgMGQFiTZQt2B5700WkbeAauDKaF9S1QsTPO4gJlQW8sLNi/nb9iMsnFhJQ0snXT19TK4uHpTW2d3H1FFW2vwJlRw63klHUNrh1i7auno5JSittbOXaaOttDMmVHCktZvjdtrb248wz05r6exh+ugS3t5+hLnjKzja3k1zew+n1pTy9vZG5o6voLm9h6Pt3ZxaU8LbO5qYO66cYx09HGnrZkZNKW/vOMKcseW0dPbQ2NrFzDFlvLPjCLPHlnO8s4fDjrTTx5bR2tkbSFux8wizasto7erl8PGBtNPGlNHe3cfBlk5Oqy3j3Z1NzBxTSnt3Hw0tHcyqLefdnU3MGFNKZ08f+5s7OH1sOe/tamL66BK6e/upt9NW7mrilNEl9PYpe5vamT1ucNqepnbmjCtn1e4mpowsoV+V3UestNW7jzJ5ZDH9quxqbGPu+ApW7znK5OoiVGGnI21SVREisP1wG/PGV7Bmz1Em+tMOtTFvQgVr9zYzYUQhHhG2HTrOGRMqA2lej7D1oJW2bm8z4yoL8XmFLQ3HmV9Xyfp9zdSWF5Cb4xmUNqa8gHyflw8OtLCgrpIN9c3UlFlp7+9vYeHESjbWH2NUaT6FuV42hUjbuP8YZ04cwab9x6guyaM4L4cN9Y604jxK8n2s29fMokmh094/0MKIolzKCnys2dvMWXZaZWEu5YX+tEo+OHCcikFpI9jc0EJZgY+KwlzW7DnKWZNHsLnhOKX5PkYU57J6t5W2paGVkvycQWkfHmylKC+H6uI8Vu5u4iN2WmGul5El+YG0rQdbKXCknTVpBNsOtZKf62V0aT7v7rTybT/cSp4vOK2NvBwPNeX5rNhhpe1obMPnFcaUF7BiRxNnTR7BzsY2cjxCbcVA2q7GNrweYWxFIe/sOMJZk0ew+0g7AoyrDJ+2aNII9ja1o8CEEYW8vX0grV+VuqqiQNq+o+309Q+knTmpkv3NHfT0KRMdaQeaO+nq7WdyddFAXXMsRFoG6qS54ytoagtdJx3r6GH66Mh1UnN7D03tQ+ukc36YzNo7MnE1UCKyANirqqtF5O+Ar2LtwfciVnDBtLL1/fWtp4wuzZZ5qyqgMdMihkE26c0mrZBderNJK2SX3mzSCjAtXQcS1eGPjonIauBCVW0SkcVYIdn/GZgDnKqqUXtREcr+JPBfWL2xZmCtql4c5TsrVTUrXNKzSStkl95s0grZpTebtEJ26c0mrZBevfEO8XlVtcl+/TngQVV9GnhaRNYmIkhVfw/8PpEyDAaDwZD9xOvF5xURf+N2AfCK47NE57UMBoPBYIi7MXkMeF1EGoEO4A0AEZlCDF58KeDBDBwzXrJJK2SX3mzSCtmlN5u0QnbpzSatkEa9cc1BQcClvAZ4UVXb7LRTgGJVXZ08iQaDwWA4GYm7gTIYDAaDIZUkupOEwWAwGAypQVXT/gDGAa8C7wObgG/a6ZXAS8BW+7nCTp8OvA10Af/iKCcfeBdYZ5dzV4RjXmuXuxW41pF+D7AXaHWzVqAEWOt4NAIPpEqvozwvsAZ43q22TVRrJmwL7AI22MdbGUHvEmALsA243ZF+g52mWFuOuVnrGw7b7geWpVhvOfAUsBn4ADjLxbZNVGvabIu1/sl5nbQANyXTtkPKiZYhFQ+suat5jsrhQ2AG8CP/jwFuB35ovx4JLMCq8JwGE6w5LwAfsAJYFOJ4lcAO+7nCfu3/M/xzaeEqUddoDcq3ClicKr2O8m4BfkuYSt8Ntk2G1nTbFqvSj3iBYjW424FJQC7Wzc0M+7O5QF24ctykNSjf08Dfp1jvw8BX7Ne5QLmLbZuQ1nTbNkhTAzAhmbYNfmRkiE9VD6jtSKGqx7HuHGqBK7D+MOznpXaeQ6r6HgMRfP3lqKq22m999kNDHPJi4CVVbVLVo1h3C0vsMt5R1QPZoNWP7YwyEtt7MhV67eOMBS4DHgppnCh602XbZGh1lJMW28bIQmCbqu5Q1W6sBfFX2GWvUdVd4b7oJq1+RKQUOB9Yliq9IlIGLAZ+YefrVtXm4ehNl22TodVRVsptG8QFwHZV3T0cvdFsG0zG56BEpA6rVV0BjHJUaA3AqBi+77UXBx/CqnxWhMhWizXU5GefnZatWq8CHlf7liRVeoEHgH/F3q0+DK6wbRK1psu2CrwoIqtE5PoE9EbFRVqXAi+raksK9U4EDgO/EpE1IvKQiBTFqTcqLtKaDts6uQpruVEokmJbyHADJSLFWN3Sm4INa1cQESsJO1+fqs4BxgILReS0k0BrpJMjKXpF5OPAIVVdFafGmHGZ1pTb1uYcVZ0HXAJ8w94yLOm4TOvVpN62OcA84KeqOhdowxq+Sjou05oO2/rLyQUuB56MT2rsZKyBEhEflrEeVdVn7OSDIlJjf16D1dOICbtr/CqwRETOFJG19uNyoB5rotDPWDst67SKyGwgJ1JlnCS9ZwOXi8gurC76+SLyiEttmxStabQtqlpvPx/C2tproYiMc+j9WjS90XCTVhGpwhr6eSHFevcB+xyjE08B81xq26RoTaNt/VwCrFbVg/Z3k2pbJxlpoEREsMZdP1DV+x0fPYflZYX9HDE2lIhUi0i5/boA+BiwWVVXqOoc+/EcVtTfi0SkQkQqgIvstGzUGvFOKVl6VfUOVR2rqnVYvYpXVPUaN9o2iVrTYlsRKRKREv9rW8dGVd3r0Psz4D1gqohMtO9ar7KPFRUXar0Sy3mlM5V6VbUB2Csi/h23LwDed6Ntk6g1LbZ1MOg6SaZth6BRvChS8QDOwepOrmfAZfFSYATwMpbb41+ASjv/aKy7jRasHc73AaXA6VhuxeuBjcB3IxzzOiz3xm3AlxzpP7LL67efv+dWrfZnO4DpqbZtUJnnEtl1O6O2TYbWdNoWy7tpHQNLDr4V4ZiXYnldbXfmA260y+vFci9+yK1a7c9eA5ak47zFiqqw0i5rGSE8Nd1g22RozYBti4AjQFmUOj4u2wY/zE4SBoPBYHAlGffiMxgMBoMhFKaBMhgMBoMrMQ2UwWAwGFyJaaAMBoPB4EpMA2UwGAwGV2IaKIPBYDC4EtNAGQwGg8GVmAbKYDAYDK7ENFAGg8FgcCWmgTIYDAaDKzENlMFgMBhcSU6mBSSDqqoqraury7QMgyGlNLf30NDSSU9fPz6vh9Gl+ZQX+jItyxCC5vYe6ps76HfsdeoRoba8IOv/s1WrVjWqanU6jpWxBkpExgG/wYriqMCDqvoTEfke8I9YkSYB/k1V/xiprLq6OlauXJlKuQZDRlm2pp47ntlAVU9fIM3n8/LtT81i6dy4gpWGPc59y7ewv7mDMeUF3HbxtKSWf7Jw9r2v0NvcMSR9VHkBb91+fgYUJQ8RCRXmPSVkcoivF7hVVWcAi7Aidc6wP/tPHYgvErFxMhhOBu5bvoUOR+ME0NHTx33LtyTtGP5GsL65AwXqmzu4+fG11N3+Amff+wrL1sQVc+6kZH+IxilSuiE0GetBqeoB4ID9+riIfECccesNhhOddFR4oRpB/wBVfXMHdzyzAcD0qGJgTHkB9SH+mzHlBRlQExtu7D27wklCROqAuYA/9PENIrJeRH5pRz4N9Z3rRWSliKw8fPhwqCyGNLFsTT1n3/sKE82ddsoIV7Els8KL1tglu8d2InPbxdMo8HkHpRX4vNx28bQw38gsy9bUc9tT6wb1nm97al3Gr+WMO0mISDHwNHCTqraIyE+B72PdvH0f+DFWFNRBqOqDwIMA8+fPDxt10Y13BbGSDdr9w0L+O+9k3WlH+u3x2sUt9oxHx3nTq3nknT0h05NFuLt+J2aIKjb8/6cbzrdYuOsPm+jpG1yN9vQpd/1hEzD4d3gKSivTpSujDZSI+LAap0dV9RkAVT3o+PznwPPxlp+qyjMdRNPulso20txIvHoi/XYgrv/ULedCvDpe3Rx6lCBcejzcdvG0QdpC4eYhKrexdG6t6+sZP0fbe8KmB5+vOaXVE9KlK2NDfCIiwC+AD1T1fkd6jSPbJ4GN8R5jOBPLbhumiqTdLZPZy9bUh73jTuROO9Jvj9dZIB1OBrEQr45w9qxv7hjWfx7pPF86t5YffGoWtXYjJCG+397dm/Frw4/brtlYyEbNQ25YRNLWbkTsQYlIxK6cqjYlcOyzgS8CG0RkrZ32b8DVIjIHa4hvF/DVeA8Q6aJetqZ+0JBRsu6uk9WziTQpHm0y+6bH13LXHzZx5ydmhjx2MjT6bRaOWO+0Q2mJxyHA+Vk8ZSa7RxquvGgNzf7mDsoKfIhYa2n83y0v9IW9y01mL9J5179sTT3fe24TzR0Dxz3a3sNNj6/lW7/fwD2fTK6L+3BwS494OLh5VKS8wDfof3YLohp2+gYR2YlV9wkwHjhqvy4H9qjqxHSIjMb8+fP12z9/dtDFVGEvhgt3URf4vPzAXkNy9r2vhOwJeEX48Wdnx3ySBJ+AwWVdfeY4/n3prLAnojPdI0JfiP+mtryA/XbPKRrO3xhJY6h80QhnMwCfR7jvM9HtFk5Lvs8T8n/z39mHOm55gY+1d17Et5dt4NF39gyyT7QyQw1txWOTaL/rB5+axX3Lt4TULxD2P/V5hN5+jfqflxf4KMrLCVvBhfvPaiOszYn4P3uF+66M/fpIJvH8llQT7boOZ8do5yAMnctaubuJx1bspU91UN2SiPbbnlxHT//AWebzCMX5OUOumwMP30TXga2hOthJJ2IDFchkzQX93r8mSUQuAZaqaty9m2QyZcZslaU/GGRcAI+A1yNDJv/8iF0rRLJAtIrKeVIiEM2cuV5BYZCmAp+XT59Ry9Or6iOO//t/U38srZNN8AUb64Ud7W5u4u0vRK0wa6M4N7R394ZsNEJV1v7/YeXuppDOAl6PcPXCcUMaJz/lBT66evuHVACfPqOWR1fsCfm/xVvZRarUywt8tHT2DOs/TISKQl+gJx3uPxNg572Xhfx+3e0vRCw/Uw1CPL8lmGT2WMLdlMR6XVeE6SGHOm8jEXzNDYfgHrO/rgm+Hg/8+pv9XQ3bvCELSTKxOkksUtV/9L9R1T+JyI9SpGnYNLR0UhXiiu9XKM3NCdt1jaFtpqOnj1ufWMfNj68dMvRy3vTqwSdfDOV1h2gsO3r6Qla6MPTkGG7Ftt8ezvRfiOG+7qxQYxk+icXjq765g9ueXAfAyt1NgxqPSN8N1ljo85Dn83Dz42vxSOgbt75+DWtDgGMdPfzn5+YMqpDOm17N4+/uDXseRBs2DFcJRPpt6R5G8U9yQ/j/rKzAFxheDP5t3jA9eT/p8uoLtn+4IU/FukGIVknHOkQYy/++bE09tz6xboidIl3XwYQb6Rnu+ZLoUGdXb3/gtb+u8Q+hKVYDuLflcNp2koi1B7UceAN4xE76ArBYVS9OobaYyauZqjXXPhD289oYKtMTmUjDR04e+NyciEOefsLdWYWjwOehs6c/pryhiPU4seIR+PyZ43lh/YGwFQMM9A78a0ScvV7/8BYMHn6pG1HAW9sTmZpNDeGGkULhrIyiXTf+oVWrwl9PR49Vwflt7Bx2SmR5QCy6w/0Wn1dC3hiGzG//+LICH929fbT39A/6PHhEJRFtqSTacG8w4RpZJ/7rQURWqer8VOgOJtYGqhK4E1hsJ/0VuCtBJ4mkEamBEuALi8bHfCdzsuPWydJM4G+w5979YsiGrNDnoadPhwwtn2xMHVnE1kNtIT+7ZtH4wLxrcEXu8wg+rwQaAedwpJNoN0yZIlrv0k1Ems8CYm5kBaj/5Q3d3Yd25qVSb+B4sTRQbieWHlS4+Q6DIRQ+D2z9v5exbE09Nz2+NvoXDGEZ7vVXUejjstNreH7dAXOzlESGO58VjnQ6SURzM39AVW8SkT8QYpRFVS9PmbIk4sa7L4O76emP7iBgiI3hXn9H23vMiEcKyMbGPpqTxP/az/+RaiEGg8FgMDiJ2ECp6ir7+XV/mr156zhVXZ9KYSKyBPgJ4AUeUtV7U3k8g8FgMLiLmLasEJHXRKTUdpZYDfxcRO6P9r14EREv8D/AJcAMrN0lZkT+lsFgMBhOJGLdU6lMVVuATwG/UdUzgQtTJ4uFwDZV3aGq3cDvgCtSeDyDwRArJ4BjlSE7iLWByrE3cf0sCewuPgxqgb2O9/sICmbojAeV1EUyBoMhMmEWSxtOEtJY38baQN0NLAe2q+p7IjIJ2Jo6WdFR1QdVdb6qzg+57bLBYDAYkk8a69uYtjpS1SeBJx3vdwCfTpUooB4Y53g/1k4zGAwGw0lCrE4Sp4jIyyKy0X5/uoh8O4W63gOmishEEckFrgKeS+HxDAaDweAyYh3i+zlwB9ADYLuYX5UqUaraC9yANaz4AfCEqm5K1fEMBoPB4D5i3c28UFXflcGTo70p0BPADu3xx1Qew2AwGAzuJdYeVKOITMb23xCRK4EDKVNlMBgMhoQp9HmoKPQhWHsiXrNofCDwZzYQaw/qG8CDwHQRqQd2YoXcMBhOWJzhNsyGsfGTl+Ohu7efsgIfbd29YQOIZgv+Xczdtgl1Ua6X9u6+YQdfdPOek7F68e0ALhSRIqxeVzvWHFTaAle5GX+snZsfX5u2JQIeoD9qLvfg8wqfWzCOVzcfzorNe31eCYQiWDq3NmLIbhgIFQFEjauTStIVAsIr1nBKpEgjoUKRO2NChWqw/GHG/UFBozUAuY5YT1ZgS29KG4xQkadDRdJNd3yoAp+HTXcvSesx00G03cxLsXpPtcCzwF/s97cC64FHUy0wkzgDt4WLm1Lg8wbuVmK9y471QhIgP8TJ7q8Mw4U/dxuh4vwEh5eOhM8j3PeZ2XH1YpwB5vyVY7QGMpTeUMH+goPX+Yn0v+TleAZFLXVSXuDje5fPDBmvJxZbPfC5ORFtFFxxClCY66Wte/iVaWlB6Ii2/nLDhV1fOrd2WBFrwwUEDBUQ0c9w40fF2qj7r/Xg3wNDYyzFcp45cV7Tj63YS58qXhEWTapg15EO6ps7AjqDA3g6Yz2daESMByUizwJHgbeBC4CRWOffN1XVNWMekeJBhQrAF2uUTX+wNSeRLqhYLgx/JFZnRM7bnlxLT4g665pF45k/oTLqBeysvPzRblOFz0NIraEIvtsMRbB+fyygVzcfHvKbw9nXK8KPPzt7UCMULZJoqOOGCpYX/J1Yyg6ns8Dn4QefOj1sIxKuYo8lamu0WD+1joozlmB1hT4Pnb39Ec+lcJGOY/nfh8NwI/GG69XMG1/G37Y3hazcwzUoXhH6VYc9bGZd1+vCBrN0DhMOp1x/2fFEJg7HcBv0dMaDitZAbVDVWfZrL5ZjxHhV7UyHuFgJ10BFu1CihTke7oUW7aSMVO63l20YdOcUPDQyHA3BF6ezJxhuyETE2mIt3N1ZpLveuhEFYS/8RC6cWH5bKo6TKBNvfyFkxe1vgMJF6A13vkWrQAp8XvJ9nrA9mmg2ClfhxTL3Fsv5kgki/aZw6ck+t+K5CcoE0eotf1Ryf/104Nc30dXgjgZqtarOC/feLdRMman5V/4orgsl0kUYaagiUnnRhmPiKXe4GsLdYUW7EOO9O0v2XV2mj5MI4RoUp9PFcCrDcA0eDPQeI81/+kPXx0Msd9e15QWu/j9iJdnnVjacq36Wrann5ifWhtwHOPjGSURWqer8dOiK1kD1AW3+t0ABloOEAKqqpXEdVOQ+4BNAN7Ad+JKqNotIHdbC3C121ndU9WvRyps/f75+++fPxn0yDPeONlaiVVSZIpsunGwklgZoOP9BpEbCf7OTqnMt2vBips9lt5ItvX0nsWpOZwMVLWChN0XHfQm4Q1V7ReSHWLtU/B/7s+2qOme4BQZPvg6HOz8xM6zzQyKEm1hPtNxEScRWhuiEmzh32nw4/8F506vDOl2Msde0pOpc82sMNSrghnPZrdy3fMuQRr2jp4/7lm9x7bUXy3mbbmJdB5VUVPVFx9t3gCszocNPqv4YN/7hhvSQzJuAVzcfDpkuMMgVHlJzrvl/i+l5x87+MD3ecOluwW03rxGH+NIiQOQPwOOq+og9xLcJ+BBoAb6tqm+E+d71wPUA48ePP2P3brMky3BiEmkOalcK5zIN8ePW4f1kkM4hvli3Oho2IvIXEdkY4nGFI8+3sPb086+n8nsJzgVuAX5rr8UagjMeVHV1dap+hsGQcfzDeMFk05Y1Jxu3XTyNAt/gGRIzJDp8MtaDEpF/AL4KXKCq7WHyvAb8i6qujFLWcQYcK9xOFdCYaRHDIJv0ZpNWiFGvp6C0Mqe0egIiAzeUqv29LYd393e0NKVSoIMT0rapxFNQWuktrqwVb06u9vV297U21Yf5vzKudZhMU9WSdBwoI3NQIrIE+Ffg75yNk4hUA02q2mdH7Z0K7IihyC3p6nImioiszBatkF16s0krZJfebNIK2aU3m7SCpTddx8pIAwX8N5AHvGSH8PC7ky8G7haRHqyt5r6mqum6QzQYDAaDi8iUF9+UMOlPA0+nWY7BYDAYXEjKnCTSzIOZFjAMskkrZJfebNIK2aU3m7RCdunNJq2QRr0ZdzM3GAwGgyEUJ0oPymAwGAwnGKaBMhgMBoM7UdW0P4BxwKvA+1g7R3zTTq/E2qdvq/1cYadPx4pJ1YW1LspfTj7wLrDOLueuCMe81i53K3CtI/0eYC/Q6matQAmw1vFoBB5IlV5HeV5gDfC8W22bqNZM2BbYBWywj7cygt4lWGv8tgG3O9JvsNMUqHK51jcctt0PLEux3nLgKWAz1ubTZ7nYtolqTZttgWkMvk5agJuSadsh5UTLkIoHUAPMc1QOHwIzgB/5fwxwO/BD+/VIYAFWhec0mADF9msfsAJYFOJ4lVjrqSqBCvu1/89YZOsJV4m6RmtQvlXA4lTpdZR3C/BbwlT6brBtMrSm27ZYlX7ECxSrwd0OTAJysW5uZtifzQXqwpXjJq1B+Z4G/j7Feh8GvmK/zgXKXWzbhLSm27ZBmhqACcm0bfAjI0N8qnpAVVfbr49j3TnUAldg/WHYz0vtPIdU9T2gJ6gcVdVW+63PfmiIQ14MvKSqTap6FOtuYYldxjuqeiAbtPoRkVOwTqIh+xQmS699nLHAZcBDIY0TRW+6bJsMrY5y0mLbGFkIbFPVHaraDfzOPhaqukZVd4X7opu0+rG3LTsfWJYqvSJShrWe8hd2vm5VbR6O3nTZNhlaHWWl3LZBXIAVeSLUJqhx2zaYjM9B2RvEzsXqUYxyVGgNwKgYvu8VkbXAIazKZ0WIbLVYQ01+9tlp2ar1KqwNdkM1cEnTCzyAteNHpCDvrrBtErWmy7YKvCgiq+yNj+PVGxUXaV0KvKyqLSnUOxE4DPxKRNaIyNHOlowAAAKWSURBVEMiUhSn3qi4SGs6bOvkKuCxMJ8lxbaQ4QZKRIqxuqU3BRvWriAiVhJ2vj614keNBRaKyGkngdZIJ0dS9IrIx4FDqroqTo0x4zKtKbetzTlqRae+BPiGiCyOU29EXKb1alJv2xxgHvBTtTadbsMavko6LtOaDtv6y8kFLgeejE9q7GSsgRIRH5axHlXVZ+zkgyJSY39eg9XTiAm7a/wqsEREzhSRtfbjcqAea6LQz1g7Leu0ishsICdSZZwkvWcDl4vILqwu+vki8ohLbZsUrWm0Lapabz8fAn6PdcMyzqH3a9H0RsNNWkWkCmvo54UU690H7HOMTjwFzHOpbZOiNY229XMJsFpVD9rfTaptnWSkgRJrA75fAB+o6v2Oj57D8rLCfn42SjnVIlJuvy4APgZsVtUVqjrHfjwHLAcuEpEKEakALrLTslFrxDulZOlV1TtUdayq1mH1Kl5R1WvcaNskak2LbUWkSERK/K9tHRtVda9D78+A94CpIjLRvmu9yj5WVFyo9Uos55XOVOpV1QZgr4j441pcALzvRtsmUWtabOtg0HWSTNsOQaN4UaTiAZyD1Z1cz4DL4qXACOBlLLfHvwCVdv7RWHcbLUCz/boUOB3LrXg9sBH4boRjXofl3rgN+JIj/Ud2ef328/fcqtX+bAcwPdW2DSrzXCK7bmfUtsnQmk7bYnk3rWNgycG3IhzzUiyvq+3OfMCNdnm9WO7FD7lVq/3Za8CSdJy3wBxgpV3WMkJ4arrBtsnQmgHbFgFHgLIodXxctg1+mK2ODAaDweBKMu7FZzAYDAZDKEwDZTAYDAZXYhoog8FgMLgS00AZDAaDwZWYBspgMBgMrsQ0UAaDwWBwJaaBMhgMBoMr+X8RaVfWJz8VnwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8gSJ9WqZ4U8As4LuqOhM4gjuFVVYxizOKfs3XUwFcDfyyd6GWLjYJSkTSuM77qao+4It3isg4P38c7kijJP5Q+WlgnohcJCIr/eNqenhtvzjHKiLnAamuNshlivXtwNUishl3uH65iPwkpv1allgj6ldUdbt/3gU8iNtRmRiI9RPdxVpMnOIUkTrc6Z9HQ453G7AtcEbiPmBWDPu1LHFG2K9584EXVHWnf2/Z+rWzWCQoERHcedg1qnp7YNYjuJFW+OeHu6lntIiM9NPVwJXAWlVdrqrn+8cjuEsmzRWRUSIyCpjrywZirF3uOZUrVlW9TVUnqOpk3FHFb1T1A3Hs1zLGGnq/ishQERmen/YxrFbVrYFYv0fgWpR+7/V631ZRMYzzPbhBK8fDjFdVdwBbRSR/UdMrgFfi1q9ljDOSfg3o8N0oV78WpN2MoojiAVyMO7xcRfsQxgXAKcAy3DDIp4Bav/xY3N7HQeCAn64B3owbWrwKWA38XZE2P4wb7rgBuClQ/h1fX84/fzWusfp5m4DpYfZrpzovpfjQ7X7t13LEGlW/4kY5vUj7vxp8qUisC3CjrzYGlwM+5evL4IYZ3xXHOP28Z4B5YW8H/LzzgQZf10MUGKHZ3/1ajjj7oV+HAnuBEd1s03vcr4UediUJY4wxsRSLU3zGGGNMZ5agjDHGxJIlKGOMMbFkCcoYY0wsWYIyxhgTS5agjDHGxJIlKGOMMbFkCcoYY0wsWYIyxhgTS5agjDHGxJIlKGOMMbFkCcoYY0wshZag/D1CnhaRV0TkZRH5tC+vFZGlIrLeP4/y5SIi/ywiG0RklYjMCtS10C+/XkQWdtWmMcaYwSO0q5n7G2CNU9UX/L1kGnG3Jf4QsE9VvyUit+IuL/8FEVkA/A/cZdovAu5U1YtEpBZ3OfrZuEvGNwJvUdX9oQRujDEmFlJhVazuXvdNfvqQiKzB3Zf+Gtx9egCW4O5l8gVf/mN1GfM5ERnpk9ylwFJV3QcgIkuBeRS5vXFdXZ1Onjy5/B/KGGNOco2NjXtUdXQUbYWWoIJEZDIwE1gO1PvkBbADqPfT44Gtgbdt82VdlXduYxGwCGDSpEk0NDSU7wMYY05ah4638r8eW0sup/zX2ROYPbm2v0PqVyKyJaq2Qk9QIjIMuB+4RVUPursPO6qqIlKWc4yquhhYDDB79my7C6MxpixWbXuDe/70GgBHW7MnfYKKUqij+EQkjUtOP1XVB3zxTn/qLv871S5fvh2YGHj7BF/WVbkxxoQum2vf383lbN83SmGO4hPgh8AaVb09MOsRID8SbyHwcKD8Rj+abw7whj8V+AQwV0RG+RF/c32ZMcaELhcYSJYLaVCZKSzMU3xvBz4IvCQiK33ZF4FvAb8QkY8AW4D3+nmP4UbwbQCOAjcBqOo+Efk68Lxf7mv5ARPGGBO2YE6yBBWtLhOUiFxX7I2BU3Zdzf89IF3MvqLA8grc3EVddwN3F2vPGGPCEDzFl831YyAnoWJHUH/pn8cAbwN+419fBvwBKJqgjDFmMMgfNYlAWP83agrrMkGp6k0AIvIkMCM/NNwPbPhRJNEZY0w/yx9ApRJip/giVsogiYmB/1sC2AlMCikeY4yJlfxRUzIhZC0/RaqUQRLLROQJ2q/c8D7gqfBCMsaY+Mj6BJVOJOwUX8S6TVCq+kk/YOIvfNFiVX0w3LCMMSYe2k7xJe0UX9RKGmbuR+zZoAhjzEmn/RRfosOIPhO+bn+DEpHr/G0u3hCRgyJySEQORhGcMcb0t3xSSicFy0/RKuUI6jvAX6rqmrCDMcaYuAme4rPfoKJVyii+nZacjDEnq/zvTik7xRe5Uo6gGkTkXuAhoDlf2N2VJIwxZjAIDjO3/BStUhJUDe7aeHMDZYoNmjDGnATylzdKJewUX9RKGWZ+UxSBGGNMHOVP8aWTCTuCilgpo/jOEpFlIrLav36ziHw5/NCMMab/dbiShGWoSJUySOIHwG1AK4CqrgKuDzMoY4yJi47DzC1BRamUBDVEVf/UqSwTRjDGGBM37ReLTWD5KVqlJKg9InIGbmAEIvIeoKn4W4wxZnBoG2aelLbr8plolDKK72ZgMTBdRLYDfwbeH2pUxhgTE/mclLTbbUSuaIISkSTw16r6ThEZCiRU9VA0oRljTP/LBv5R1/JTtLo8xSciKVXNAhcDqOqRniQnEblbRHblR//5sloRWeqv7bdUREb5chGRfxaRDSKySkRmBd6z0C+/XkQW9upTGmNML7UPM7dRfFEr9htUfmDEChF5REQ+6C8ce52//UZ3fgTM61R2K7BMVacCy/xrgPnAVP9YBHwXXEIDvgJcBFwIfCWf1IwxJgoD6RTfD3//Zz5z70q+99uNJS3f9MYxFtz5LPPu+B33Pv9ayNH1XCm/QVUBe4HLcQMlhBKuJKGqvxORyZ2KrwEu9dNLgGeAL/jyH6v7h4PnRGSkv7X8pcBSVd0HICJLcUnvHorYc7iZLz34EulkAhE43prjcLMbeJhOChXJBJWpBMOr0ojA0Zasn5egIimkkwnSqUTH176sIpmgIuXKsjmlNasMrUwyvDLN0MokFX4ZBBIiJERIipBIuBU8IYIICEJCXEeqgqLuWd0phWzOPVoyOVqzOfceEQRoyeQ4nsnS3Jojp0pOocJ/1pZsjpZMjuaMe3bT2Q5l+dcZvzco4vpNkMC0czyT42hLhqMtWVTzn8mPmPGxpxJCTXWKkUMqqEonyWRd3dmckskpmWyOrCoVyQRV6SRDK5IMqUiBuHbynytfZ2UqQXU6yZCKJFUVSVK+3/KxJhPCiOo0tUMrqEwlO693tGaVlqzrm6QIyYT7XEebs7Rkc1SmEiQSwvEW97o162JsyebIZJVWX9aazZHJ5WjJKJmc+zu0ZpTWXI7WTK6tr/Mk33md+jTn/57N/m+W/9spkPCfR/LriUDCf9583Pl1KJ1MUJFKnPA3CraXEKEilSCVSHS4uGndsEqGVqbI5pSECGm/XqeSQnU6ybDKFEdbshxrzZJKCFXpJFXpJAlxw6yzqm6dTbg2cqrkcrR9tvx6m3+dyeb7LP86R2tOyQbKWn1/Z3OuT917tG39yc/Pf76c+u9IYN1T/yqXUw4dz3CoOYPg+tR979q/f/k+bfte+jIXT45UMtG2/g2vSlOdTpBMJljx2oG27cOR5gz/78XXSSWC24nANsJvX4LzsjnlWEuWoZUp6muqONKcOWGwRf570Oq/p6cMq+B4q9suVaeT7D7czOhhlSQTwrb9x2jO5Nq2Efn+a8nk+Majr5AQIbNCmVI3lIS49luyOZpbs23rdWvWtfOrVU2saTpITVWKL9z/EhdMruU3a3exdd9RKtNJRKC5Nde2HtYOrSBKxRLUGBH5DLCa9sSU19vdiPrA7eN3APV+ejywNbDcNl/WVXlRR1uy/Hr1DlqzOVShMp1keFXKbdz9H+dYS5bDzRkUtwIkRE7Y4Ax2qUT7n9QlSg1Mu/KqdIIhFSmq08m2f1TMqbZ9oQBaszkOHm/leGvhvksl3IYi//cop0RgwxxWG+WSENo2/JWpRNvGI6fatqPR9jrnX6uigXknm/zf1yVzl8AFQXEJM7+TkxBheFWKYVUpCOzkBfsxl2tPoDltT67pZKItEYPbKevc1+NGVDFmeCX7j7byP+5Z0evPM2pImv1HW0v63DmFc8bXMKl2CI+9tIP3vGUCs08bxa0PvFT0vR+YM4kf/3ELH/+PxpJiOqt+GO8691T+6alXuXPZeh5e+TqVfkcopy5hg9sJmDZ2eEl1lkuxBJUEhtExMeX1+ZuiqioiZfvGicgi3OlBJk2aROPfXtnbuNqOjPLJLL/n3JLN0pJxeyAt2RzJhJBKCEdbshw+nuFwc8YdtWRzbRv8XE7JKv5Z21b8/EbHbejbN/bBPbtkQqhMJUknpa0+VahIuSORimSCZNIdfeT3SitT7uiwIpWgMpX0z4lOz67O4B5/ORxvdUcGqaS09U3+6CD/mZszOY62ZDnakmlLJJ0TSnPG7ckfbclyrCXr9tBp34POqXLgaCt7DjfTknF7d/k9yfwRbGW6PQFk/fuGVCRJJxO0ZNzRVb4P0ykhlfBHzIHp4FFGRTJBKtlxb7nSH2WLdNybD34eVdzRs0jbHnpv5Y+oob2tYHvql8kEjgAT/vpxuw81c6wl23bB07Yjwqzbuz/UnGFoRZLqdJJMTjnemuV4axal/WgkmDDzR3zJBO1Hf4H1NpVIkEoIqaS0Hc2lk0LSl6eTCZIJV5ZK+mUTgemkkE64I92oqSrH/LrcmsshuCP2ZEJ43wUT284MBI+02x9++5BxR3/N2RyphFCZSrB2xyF2vHGcumGVnDqyqlOb7m+aTiZozebYtv8YP/7jFtY2HWrb8buvcRu1QytIJoTb33seqUSCZIK2/k0lElRXJDl/4kjed8FEWrPa9nepTCXb1teKwFGfS0Sujx9+cTuv7TsKwN++ewYfmHNawf6Rm0Ps/E6KJagmVf1amdvbKSLjVLXJn8Lb5cu3AxMDy03wZdtpPyWYL3+mUMWquhg3HJ7Zs2f3OvGJ+C9VEqpJdv8G0yZ/dNAVkfZTR1GfKhgMkgmhuqJ36+SEUUPKHM3gJSIMqUgxpMAqevroYZHFUV9Txf9+Yh2vHzjWVrbitf0Mr0pxzfnFTySdfeqIHrc3akgF2/a7BDW8qqSbrYeu2C5dGLsujwD5kXgLgYcD5Tf60XxzgDf8qcAngLkiMsoPjpjry4wxZlA7q96dTjvakuWdb3K/hqzdcSi05FFTlWLXoWY/nQ6ljZ4q9kmv6EvFInIP7uinTkS24UbjfQv4hYh8BNgCvNcv/hiwANiAu7XHTQCquk9Evg4875f7Wn7AhDHGDGZXzqhn1Vfn0tya4/UDx3hqzU4OHc8wfmR1KO3NnlzLC68dYGxNFdPHRftbU1e6TFB9TQSqekMXs05IfH70XsEzm6p6N3B3X2IxxpiBqKYqDVW0jUIGeMdZo0Np6+bLzuTmy84Mpe7eiseJRmOMMV2aMKqaBeeOZf+RVm64cFJ/hxMZS1DGGBNz6WSCf3v/W/o7jMjJYLyFsYgcAtb1dxwlqgP29HcQJbJYwzFQYh0oceYNpHgHUqzTVDWSH6kG6xHUOlWd3d9BlEJEGizW8rNYy2+gxJk3kOIdaLFG1Vbf/nPQGGOMCYklKGOMMbE0WBPU4v4OoAcs1nBYrOU3UOLMG0jxWqwFDMpBEsYYYwa+wXoEZYwxZqBTf3Xi/nzgLhT7NPAK8DLwaV9eCywF1vvnUb58OvBHoBn4bKCeKtyNFl/09fx9kTYX+nrXAwsD5d/A3eLjcJxjBYYDKwOPPcAdYcQaqC8JrAB+Fdd+7WusUfcrsBl4ybfVUCTWebh/ndgA3Boo/6QvU6AuxnE+G+jT14GHwlwHgJHAfcBaYA3w1pj2a1/jjKxfgWl0/G4cBG4pV78WrKe7BaJ4AOOAWYENxKvADOA7+Q+Hu/vut/30GOAC3EYv2IECDPPTaWA5MKdAe7XAJv88yk/n/zhzfDxdbUhjE2un5RqBS8KINVDfZ4Cf0cVGPw79Wo5Yo+xX3Ia/6BcVl2w3AqcDFbidmhl+3kxgcqF64hRnp+XuB24Mcx3A3RD1o366AhgZ037tU5xR92unmHYAp5WrXws9YnGKT1WbVPUFP30ItycxHnen3SV+sSXAtX6ZXar6PNDaqR5V1cP+Zdo/tECTV+Hv1Kuq+3F7D/N8Hc9p+00VYx1rnoichVupng0jVt/GBOBdwF1d9U2xWKPq13LEGqgn9H4t0YXABlXdpKotwM99W6jqClXdXOhNcYozT0RqcHfnfiiseEVkBHAJ8EO/XIuqHuhJvFH0azniDNQVer92cgWwUVW39CTeYv1aSCwSVJC/TfxM3BFFV3fgLfb+pIisxN1raqmqLi+wWK/u1BvjWK8H7lW/ixJGrMAdwOeBYrccjkW/ljHWKPpVgSdFpNHfdLO3sRYVozivBZap6sEQ450C7Ab+XURWiMhdIjK0l/EWFaM4o+jXoOuBe7qYV5btAMQsQYnIMNxh6i2dO9pvJLrcUASWy6rq+bibG14oIuecBLEWW1n6HKuIvBvYpcZ2i1IAAB3HSURBVKql3UO6D2IWa6j96l2sqrOA+cDNInJJH+IdCHHeQJE+LVO8KWAW8F1VnQkcwZ3CKquYxRlFv+brqQCuBn7Zu1BLF5sEJSJpXOf9VFUf8MU7/Z136XQH3m75Q+WngXkicpGIrPSPq+n6Dr4DLlYROQ9IdbVBLlOsbweuFpHNuMP1y0XkJzHt17LEGlG/oqrb/fMu4EHcjsrEQKyf6C7WYuIUp4jU4U7/PBpyvNuAbYEzEvcBs2LYr2WJM8J+zZsPvKCqO/17y9avncUiQYmI4M7DrlHV2wOzuroDb1f1jBaRkX66GrgSWKuqy1X1fP94hD7cqTeGsXa551SuWFX1NlWdoKqTcUcVv1HVD8SxX8sYa+j9KiJDRWR4ftrHsFpVtwZi/R7uhp1TRWSK33u93rdVVAzjfA9u0MrxMONV1R3AVhGZ5ouuAF6JW7+WMc5I+jWgw3ejXP1akHYziiKKB3Ax7vByFe1DGBcApwDLcMMgnwJq/fJjcXsfB4EDfroGeDNuaPEqYDXwd0Xa/DBuuOMG4KZA+Xd8fTn//NW4xurnbQKmh9mvneq8lOJDt/u1X8sRa1T9ihvl9CLt/2rwpSKxLsCNvtoYXA74lK8vgxtmfFcc4/TzngHmhb0d8PPOBxp8XQ9RYIRmf/drOeLsh34dCuwFRnSzTe9xvxZ62JUkjDHGxFIsTvEZY4wxnVmCMsYYE0uWoIwxxsSSJShjjDGxZAnKGGNMLFmCMsYYE0uWoIwxxsSSJShjjDGxZAnKGGNMLFmCMsYYE0uWoIwxxsSSJShjjDGxZAnKGGNMLKXKWZmIzCo2X1VfKPLeicCPcbcdVmCxqt4pIl8FPoa7NTLAF1X1sfJEbIwxJq7KersNEXm6yGxV1cuLvHccME5VX/A3R2sErgXeCxxW1f9TtkCNMcbEXlmPoFT1sj68twlo8tOHRGQNML43ddXV1emIMadSnU5yuDlDdTqJAi2ZHNUVSY61ZKlIJRCBYy1ZhlamON6aJZUQkokER1oyDKtM0ZLJAVCRSnC4OcOwihSt2Rw5VSrTSY40ZxhSkSKbUzLZHFUVSY62ZKlKuTOnxzM5hvqydDJBMiEcbckwtDJFc2uWhAjpZILDXbQ3tCJFNpcjk1Oq2tpLklP3WYZUJDnSkqUylUCAY63Bz9K5vRwJwbXX7NpznwUq04nCn6U5Q5Xvu+bAZ6lIJUgIHPV919yaJZEQ0omuP8uwihStuRy5XLDvkuRy0JI98bMcb80xpDLJ8ZYsqRP6LvBZAu0pUJkKfpaOfVddkUS19PWgrb1MDim4HhTruyxV6a7Wg059V8J6kMnlyHaxHlT7uoPrwbDKFMf6uh7kclT79kpZD463ZkkmhFSPv0NJsjlozbZ/lrbvUMjrwZDAZyl5Pci31816UF2R5IhfD07ou2SCROf1oMTvUFfrwYnfoSxDyrE98OvB4eYMdcMqaWxs3KOqo3uzbe6p0G5YKCLnADOAqnyZqv64xPdOBn4HnAN8BvgQ7u6ODcDfqOr+Au9ZBCwCmDRp0lvkhn/rU/zGGGM6uv+/v43Zk2sbVXV2FO2FMkhCRL4C/It/XIa73ffVJb53GHA/cIuqHgS+C5yBuzVyE/CPhd6nqotVdbaqzj6lLpLkbowxJ5X8UV1UwhrF9x7gCmCHqt4EnAeM6O5NIpLGJaefquoDAKq6U1WzqpoDfgBc2H3zdht7Y4wpN5Fo2wsrQR3zCSUjIjXALmBisTeIiAA/BNao6u2B8nGBxf4LsDqEeI0xxsRMWQdJBDSIyEjcEU8jcBj4YzfveTvwQeAlEVnpy74I3CAi5+MOizYDHw8lYmOMMbESSoJS1b/2k98TkV8DNaq6qpv3/B4odADZ4/95CmnchzHGnNQiPsMX2hEUIjIeOC3fhohcoqq/C6s9Y4wx4ZKIf4QKJUGJyLeB9wGvAFlfrLih46GzAyhjjBn4wjqCuhaYpqrNIdVvjDFmkAtrFN8mIB1S3d2y36CMMab8oh5mHtYR1FFgpYgsA9qOolT1UyG114llKGOMGejCSlCP+IcxxhjTK2ENM18iIhXAWb5onaq2htFW4fajaskYY0xYwhrFdymwBPePtQJMFJGFNszcGGMGrsHyf1D/CMxV1XUAInIWcA/wlpDa68AOoIwxZuALaxRfOp+cAFT1VfpxVJ8xxpi+y0W89x/mtfjuAn7iX38Ady+naNghlDHGlF0u4h/4w0pQ/x24GcgPK38WiOwOgpafjDGm/AZFgvJXkLgduF1EaoEJ0V5VwlKUMcaUW9QjpMO6o+4zIlLjk1Mj8AMR+ac+1jlPRNaJyAYRubXYspaejDGm/KI+ggprkMQIf7v264Afq+pFuDvs9oqIJIF/BeYDM3D3iJrR5RssQxljTNlFPUgirASV8nfCfS/wqzLUdyGwQVU3qWoL8HPgmjLUa4wxpkSD5Qjqa8ATuKTyvIicDqzvQ33jga2B19t8WRsRWSQiDSLSsP/AgT40ZYwxppAdbxyPtL2wBkn8Evhl4PUm4K/CaCvQxmJgMUDluKk6LszGjDHmJHTbAy9F2l5YlzqqAj4CnA1U5ctV9cO9rHI7MDHweoIvM8YYM0iFdYrvP4CxwFXAb3EJ5VAf6nsemCoiU/xFaK/HrpZujDGDWlgJ6kxV/VvgiKouAd4FXNTbylQ1A3wS97vWGuAXqvpyWSI1xhgTS2FdSSJ/a40DInIOsAMY05cKVfUx4LG+BmaMMWZgCOsIarGIjAL+Fncq7hXgOyG1ZYwxJgJvGlcTaXthjeK7y0/+Fjg9jDaMMcZEqyIZ7R2hwrrUUb2I/FBEHvevZ4jIR8JoyxhjTDTSybBOuhUWVms/wg1oONW/fhW4JaS2jDHGRKAiFW2CCmuQRJ2q/kJEbgM3Ck9EsiG1dYJ0MsH7Zk9keFWK/9y4l7kz6vnjpr1MOWUotcMqeOqVnSw4dxyrth1gSEWKKXVDeXx1EwvOHcfmvUc5eKyV8yaM4PHVO7jiTfUcONrCxt2HeesZdTz1yk7mnH4KOVUat+zn8ulj+N363Zx9ag1DKlL8dt1u5p87luWb9jFuZBVja6p44uUdvOvNp7K26SAA08YO5/HVO5g7o54dbxxnx8HjzJ5cyxOrd/COaaM53JxhTdNBLpk6mt+s3cVbThuFCCzftI+5Z9fznxv2cOaY4dRUp3h67S7mnTOOlVsPMLI6zaTaIW2fZdPuIxxrzXLO+BE89lIT73xTPXsPN7Nl31HeevopPPHyDi4+s46WbI4Xtx7g0mljeObV3Zw3YQQVyQS/37CHq84ey3Ob9nLaKUOoG1bJk6/sZME5Y3ml6SDpZIIzxwzj8dU7uOrserbtP8a+Iy3MmjSKx1c3cdn0MRw81sqrOw/z9jPrWLZmJxdNcX3XsGUfV0yv59n1u5k+robhlSmeXuc+ywtb9jN6eCWnjqziiZdde6/uPEwmp8wYN5zHfN/tPNjM9gNHuXDKKTz58g4uOWs0R1syrN5+kEvOGs0z63Yxc+JIUskEf9i4l6vOrucPG/Zy+uihjBxSwbI1bj14cdsBhlammHJK+3rw5z1HOHQ8U3A9eNsZdTz5yk7edsYpZLI5XnjtAJdPH8NvX93NOeNrqE4n+d2re5h3zlj+9Od9jB9VzZjh7X23ZschEgJn1bevB01vHGfXoePMPq2WX6/ewaXTRnMosB4sW7uT2afVtq0HV85w68HU+hPXg1FD0kwc1b4ebNx9mOOtubb14MoZ9ew55NaDOae7vvuLqXUcb82xalv7enD+hBGkC6wHpwyrZKn/Dr28/Q0q0wnOGO3Wg3lnj+W1fUfZf7SFmZNG8evAerB+52He5teDC6fUokqH9eBN42oYVpnimXW7mXfOWBq27KN+eBVjR/jv0LnjWLfzEDmFN43tuB68fuAYF0ypbfssx1qzrN5+kHecNZqn1+1i1qRRJET446bi68HwqjSn+e/Q/HPGsXnvEQ43Z3jzhJE8/lITV7ypnv1HWti058T1YMXWA1w2za0H544fQVU6wbPrXd8t//NeJowa0mE9eKXpEKmEcFb9MB7z36HXD7SvB4+vbuKyaWM4eDzDuh0HuXjqaH6zdicXTK4F4E9/3sc731TP7zfsYdrY4Qyval8PVry2n9qhFSesB82tOc4+taat73YdambrvqNc1Hk92P4Gl/rv0HkTR7ZtD26b/ybuiWpDDoiGcG0lEXkGd+WIpao6S0TmAN9W1XeUvbECZs+erQ0N0d0f0RhjThYi0qiqsyNpK6QENQv4F+AcYDUwGniPqq4qe2OF2z8ErOt2wXioA/b0dxAlsljDMVBiHShx5g2keAdSrNNUdXgUDZX1FJ+IXABsVdUXROQdwMdxR1JP4i7wGpV1UWX4vhKRBou1/CzW8hsoceYNpHgHWqxRtVXuX7y+D7T46bcBX8Ldx2k//kKuxhhjTCnKPUgiqar7/PT7gMWqej9wv4isLHNbxhhjBrFyH0ElRSSf9K4AfhOYF9aIwUIG0tGaxRoOi7X8BkqceQMpXou1gLIOkhCRLwELcD/2TQJmqaqKyJnAElV9e9kaM8YYM6iVfRSfH1I+DnhSVY/4srOAYar6QlkbM8YYM2iFMszcGGOM6TNV7fcH7m65T+Ouev4y8GlfXgssBdb751G+fDrwR6AZ+GygnirgT8CLvp6/L9LmQl/vemBhoPwbwFbgcJxjBYYDKwOPPcAdYcQaqC8JrAB+Fdd+7WusUfcrsBl4ybfVUCTWebj/7dsA3Boo/6QvU9wVXOIa57OBPn0deCjMdQAYCdwHrMXdQ+6tMe3XvsYZWb8C0+j43TgI3FKufi1YT3cLRPHAnRKcFdhAvArMwN2i41ZffivuahTg7i11AW6jF+xAwZ1KBEgDy4E5BdqrBTb551F+Ov/HyZ+i7GpDGptYOy3XCFwSRqyB+j4D/IwuNvpx6NdyxBplv+I2/EW/qLhkuxF3Z4AK3E7NDD9vJjC5UD1xirPTcvcDN4a5DgBLgI/66QpgZEz7tU9xRt2vnWLaAZxWrn4t9Ij2yn9dUNUm9b9Pqeoh3J7EeOAa3B8Q/3ytX2aXqj5P+40R8/Woqh72L9P+oQWavAp3GaZ9qroft/cwz9fxnKo2DYRY8/xvfGNwe1Nlj9W3MQF3Z+S7Os8rJdao+rUcsQbqCb1fS3QhsEFVN6lqC/Bz3xaqukJVNxd6U5zizBORGuBy4KGw4hWREcAlwA/9ci2qeqAn8UbRr+WIM1BX6P3ayRXARlXd0pN4i/VrIbFIUEEiMhmXZZcD9YGN2g6gvoT3J/3/XO3CbYCWF1hsPO50U942XzZQY70euFf9LkoYsQJ3AJ8HckWWiUW/ljHWKPpVgSdFpFFEFvUh1qJiFOe1wDJVPRhivFOA3cC/i8gKEblLRIb2Mt6iYhRnFP0adD10ed3YsmwHIGYJSkSG4Q5Tb+nc0X4j0eWGIrBcVlXPByYAF/pbzg/2WIutLH2OVUTeDexS1cZexleymMUaar96F6vqLGA+cLOIXNKHeAdCnDdQpE/LFG8KmAV8V1VnAkdwp7DKKmZxRtGv+XoqgKuBX/Yu1NLFJkGJSBrXeT9V1Qd88U4RGefnj8MdaZTEHyo/DcwTkYtEZKV/XA1sx/1wmDfBlw24WEXkPCDV1Qa5TLG+HbhaRDbjDtcvF5GfxLRfyxJrRP2Kqm73z7uAB3E7KhMDsX6iu1iLiVOcIlKHO/3zaMjxbgO2Bc5I3AfMimG/liXOCPs1bz7wgqru9O8tW792FosEJSKCOw+7RlVvD8x6BDfSCv/8cDf1jBaRkX66GrgSWKuqy1X1fP94BHczxbkiMkpERgFzfdlAjLXLPadyxaqqt6nqBFWdjDuq+I2qfiCO/VrGWEPvVxEZKiLD89M+htWqujUQ6/eA54GpIjLF771e79sqKoZxvgc3aOV4mPGq6g5gq4hM80VXAK/ErV/LGGck/RrQ4btRrn4tSLsZRRHFA7gYd3i5ivYhjAuAU4BluGGQTwG1fvmxuL2Pg8ABP10DvBk3tHgV7jYff1ekzQ/jhjtuAG4KlH/H15fzz1+Na6x+3iZgepj92qnOSyk+dLtf+7UcsUbVr7hRTi/S/q8GXyoS6wLc6KuNweWAT/n6MrhhxnfFMU4/7xlgXtjbAT/vfKDB1/UQBUZo9ne/liPOfujXocBeYEQ32/Qe92uhh/2jrjHGmFiKxSk+Y4wxpjNLUMYYY2LJEpQxxphYsgRljDEmlixBGWOMiSVLUMYYY2LJEpQxxphYsgRljDEmlixBGWOMiSVLUMYYY2LJEpQxxphYsgRljDEmlixBGWOMiSVLUMYYY2Ip1d8BhKGurk4nT57c32EYYwa4tTsO0ZrNnVCeTiaYPnZ4P0TU/xobG/eo6ugo2go1Qfnbbh8CskBGVWeLSC1wLzAZ2Ay8V1X3+7s+3om70dVR4EOq+oKvZyHwZV/tP6jqkmLtTp48mYaGhvJ/IGPMSWXKrY9S6I55AjR8611RhxMLIrIlqraiOMV3mbpbAc/2r28FlqnqVNzdHG/15fOBqf6xCPgugE9oXwEuAi4EvuJv0W2MMaE6dWR1j8pNefXHb1DXAPkjoCXAtYHyH6vzHDBSRMYBVwFLVXWfqu4HlgLzog7aGHPy+dxV06hOJzuUVaeTfO6qaf0U0ckl7ASlwJMi0igii3xZvao2+ekdQL2fHg9sDbx3my/rqtwYY0J17czxfPO6cxk/shoBxo+s5pvXncu1M20TFIWwB0lcrKrbRWQMsFRE1gZnqqqKSKFTvD3mE+AigEmTJpWjSmOM4dqZ4y0h9ZNQj6BUdbt/3gU8iPsNaac/dYd/3uUX3w5MDLx9gi/rqrxzW4tVdbaqzh49OpIBJsYYY0JUNEGJSG2xRzfvHSoiw/PTwFxgNfAIsNAvthB42E8/AtwozhzgDX8q8AlgroiM8oMj5voyY4wxg1h3p/gacb8jCTAJ2O+nRwKvAVOKvLceeNCNHicF/ExVfy0izwO/EJGPAFuA9/rlH8MNMd+AG2Z+E4Cq7hORrwPP++W+pqr7evIhjTHGDDxFE5SqTgEQkR8AD6rqY/71fNpH33X13k3AeQXK9wJXFChX4OYu6robuLtYe8YYYwaXUn+DmpNPTgCq+jjwtnBCMsYYY0ofxfe6iHwZ+Il//X7g9XBCMsYYY0o/groBGI0bifcgMMaXGWOMMaEo6QjKD0r4dMixGGOMMW2KJigRuUNVbxGR/wcnXjNRVa8OLTJjjDEnte6OoP7DP/+fsAMxxhhjgrobZt7on3+bL/P/LDtRVVeFHJsxxpiTWEmDJETkGRGp8VePeAH4gYjcHm5oxhhjTmaljuIboaoHgetwt8S4CHhneGEZY4w52ZWaoFL+wq7vBX4VYjzGGGMMUHqC+hruAq0bVfV5ETkdWB9eWMYYY052pf4f1C+BXwZebwL+KqygjDHGmFIHSZwlIstEZLV//WZ/6SNjjDEmFKWe4vsBcBvQCuCHmF8fVlDGGGNMqReLHaKqf/L3dsrLhBCPMcaYXnpoxXb+9xPreP3AMU4dWc3nrppW9Hb1PV0+aqUmqD0icgb+ckci8h6gKbSoChCRecCdQBK4S1W/FWX7xhgTlYdWbOdzv1xJa67rZd5+Ri0//dhbO7zntgde4lhrFoDtB45x2wMvARRMOoWWv+Xeldxy70oApo4ZytLPXFqmT9Q74u4T2M1CbtTeYtw9oPYDfwber6pbwg2vrf0k8CpwJbANd3fdG1T1lULLV46bquMW3hFFaMYYc1JpWnILzU3rpfsl+67UUXybgHeKyFDc71ZHcb9BRZKggAuBDT4OROTnwDVAwQRljDFm4Cs6SMJf3ug2Efm/InIlLjEtBDbg/mk3KuOBrYHX23yZMcaYQaqUq5nvB/4IfAz4EiDAf1HVlSHH1iMisghYBFAx9sx+jsYYY0xfdZegTlfVcwFE5C7cwIhJqno89Mg62g5MDLye4MvaqOpi3O9kVI6b2v0Pa8YYY2Ktu/+Das1PqGoW2NYPyQncoIipIjJFRCpwv3890g9xGGOMiUh3R1DnichBPy1AtX8tgKpqTajReaqaEZFP4q4HmATuVtWXo2jbGGNM/yhpmPlAM3v2bG1oaOjvMIwxA9iUWx+lu63j5m+9K5S2O/+PUiHjR1bzn7deXpb2uvqsAvy502cUkUZVnV2WhrtR6qWOjDHmpHLqyOqi85MS3r8CXTtzPN+87lzG+xg6t1SdTvK5q6aVrb2uPmt3fRC2QXkEJSKHgHX9HUeJ6oA9/R1EiSzWcAyUWAdKnHl9ijdRXVObqhkz5YTs4GWPHdqdfWPna72tv5OisSaqa2qTw2rHSzJVodlMS/bwvu25Ywf3lalt/1lHn4ZI+0GLai5zcPeWAu1MU9Xh5Wq7mFIvdTTQrIvqELSvRKTBYi0/i7X8BkqceQMp3oEWa1Rt2Sk+Y4wxsWQJyhhjTCwN1gS1uL8D6AGLNRwWa/kNlDjzBlK8FmsBg3KQhDHGmIFvsB5BGWOMGeAsQRljjIknVe33B+5CsE/j7u/0MvBpX14LLAXW++dRvnw67grrzcBnA/VUAX8CXvT1/H2RNhf6etcDCwPl38Dd2uNwnGMFhgMrA489wB1hxBqoLwmsAH4V137ta6xR9yuwGXjJt9VQJNZ5uP/t2wDcGij/pC9ToC7GcT4b6NPXgYfCXAeAkcB9wFpgDfDWmPZrX+OMrF+BaXT8bhwEbilXvxasp7sFongA44BZgQ3Eq8AM4Dv5DwfcCnzbT48BLsBt9IIdKMAwP50GlgNzCrRXC2zyz6P8dP6PM8fH09WGNDaxdlquEbgkjFgD9X0G+BldbPTj0K/liDXKfsVt+It+UXHJdiNwOlCB26mZ4efNBCYXqidOcXZa7n7gxjDXAWAJ8FE/XQGMjGm/9inOqPu1U0w7gNPK1a+FHrE4xaeqTar6gp8+hNuTGI+7a+4Sv9gS4Fq/zC5VfZ7A1dZ9uarqYf8y7R9aoMmrgKWquk9V9+P2Hub5Op5T1aaBEGueiJyFW6meDSNW38YE4F3AXV31TbFYo+rXcsQaqCf0fi1R2x2lVbUFyN9RGlVdoaqbC70pTnHmiUgNcDnwUFjxisgI4BLgh365FlU90JN4o+jXcsQZqCv0fu3kCmCjqha6q3qv+rWQWCSoIBGZjMuyy4H6wEZtB1BfwvuTIrIS2IXbAC0vsFhZ7tAbo1ivB+5Vv4sSRqzAHcDngVyRZWLRr2WMNYp+VeBJEWn0N93sbaxFxSjOa4FlqnqQIvoY7xRgN/DvIrJCRO4SkaG9jLeoGMUZRb8GXQ/c08W8st0BPVYJSkSG4Q5Tb+nc0X4j0eWGIrBcVlXPx93U8EIROeckiLXYytLnWEXk3cAuVW3sZXwli1msofard7GqzgLmAzeLyCV9iHcgxHkDRfq0TPGmgFnAd1V1JnAEdwqrrGIWZxT9mq+nArga+GXvQi1dbBKUiKRxnfdTVX3AF+8UkXF+/jjckUZJ/KHy08A8EblIRFb6x9WUcIfegRKriJwHpLraIJcp1rcDV4vIZtzh+uUi8pOY9mtZYo2oX1HV7f55F/AgbkdlYiDWT3QXazFxilNE6nCnfx4NOd5tuJur5s9I3AfMimG/liXOCPs1bz7wgqru9O8tW792FosEJSKCOw+7RlVvD8x6BDfSCv/8cDf1jBaRkX66GrgSWKuqy1X1fP94BHfjw7kiMkpERgFzfdlAjLXLPadyxaqqt6nqBFWdjDuq+I2qfiCO/VrGWEPvVxEZKiLD89M+htWqujUQ6/fo5R2lYxjne3CDVgrelbuM68AOYKuI5O9HcQXwStz6tYxxRtKvAR2+G+Xq14K0m1EUUTyAi3GHl6toH8K4ADgFWIYbBvkUUOuXH4vb+zgIHPDTNcCbcUOLVwGrgb8r0uaHccMdNwA3Bcq/4+vL+eevxjVWP28TMD3Mfu1U56UUH7rdr/1ajlij6lfcKKcXaf9Xgy8ViXUBbvTVxuBywKd8fRncMOO74hinn/cMMC/s7YCfdz7Q4Ot6iAIjNPu7X8sRZz/061BgLzCim216j/u10MMudWSMMSaWYnGKzxhjjOnMEpQxxphYsgRljDEmlixBGWOMiSVLUMYYY2LJEpQxxphYsgRljDEmlv4/ErSYScYccwEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Egdt98X6KXOu",
        "outputId": "320c099f-d767-4c4c-b1dc-4919c86e0f6b"
      },
      "source": [
        "# From the above graph’s observations, it looks like everything other than meanpressure is already stationary\n",
        "\n",
        "# To re-confirm stationarity, we will run all columns through the ad-fuller test\n",
        "adfuller(time_series_train[\"meantemp\"])\n",
        "adfuller(time_series_train[\"humidity\"])\n",
        "adfuller(time_series_train[\"wind_speed\"])\n",
        "adfuller(time_series_train[\"meanpressure\"])\n",
        "\n",
        "# Consolidate the ad-fuller tests to test from static data\n",
        "temp_var = time_series_train.columns\n",
        "print('significance level : 0.05')\n",
        "for var in temp_var:\n",
        "    ad_full = adfuller(time_series_train[var])\n",
        "    print(f'For {var}')\n",
        "    print(f'Test static {ad_full[1]}',end='\\n \\n')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "significance level : 0.05\n",
            "For meantemp\n",
            "Test static 0.27741213723016067\n",
            " \n",
            "For humidity\n",
            "Test static 0.004470100478130771\n",
            " \n",
            "For wind_speed\n",
            "Test static 0.0025407221531462514\n",
            " \n",
            "For meanpressure\n",
            "Test static 0.0\n",
            " \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "TigpPA-CKcsw",
        "outputId": "19c234a7-5bdd-4e99-8fc9-43ad526f543f"
      },
      "source": [
        "# With the ad-fuller test, we can now conclude that all data is stationary since static tests are below significance levels. This also rejects the hypothesis that meanpressure was non-static.\n",
        "\n",
        "# Let us now move towards training and validating the prediction model\n",
        "from statsmodels.tsa.vector_ar.var_model import VAR\n",
        "train_model = VAR(time_series_train)\n",
        "fit_model = train_model.fit(6)\n",
        "# AIC is lower for lag_order 6. Hence, we can assume the lag_order of 6.\n",
        "fix_train_test = time_series_train.dropna()\n",
        "order_lag_a = fit_model.k_ar\n",
        "X = fix_train_test[:-order_lag_a]\n",
        "Y = fix_train_test[-order_lag_a:]\n",
        "\n",
        "# Model Validation\n",
        "validate_y = X.values[-order_lag_a:]\n",
        "forcast_val = fit_model.forecast(validate_y,steps=order_lag_a)\n",
        "train_forecast = DataFrame(forcast_val,index=time_series_train.index[-order_lag_a:],columns=Y.columns)\n",
        "train_forecast"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meantemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>meanpressure</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-12-27</th>\n",
              "      <td>17.348792</td>\n",
              "      <td>70.642850</td>\n",
              "      <td>7.421823</td>\n",
              "      <td>1114.035254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-28</th>\n",
              "      <td>17.726869</td>\n",
              "      <td>68.599848</td>\n",
              "      <td>6.255075</td>\n",
              "      <td>1010.853957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-29</th>\n",
              "      <td>17.496228</td>\n",
              "      <td>70.909252</td>\n",
              "      <td>6.333213</td>\n",
              "      <td>992.402101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-30</th>\n",
              "      <td>17.648930</td>\n",
              "      <td>72.704921</td>\n",
              "      <td>5.795082</td>\n",
              "      <td>1004.788372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31</th>\n",
              "      <td>17.642094</td>\n",
              "      <td>73.481324</td>\n",
              "      <td>5.946337</td>\n",
              "      <td>1031.434268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>17.884142</td>\n",
              "      <td>72.291600</td>\n",
              "      <td>5.717422</td>\n",
              "      <td>1026.203648</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             meantemp   humidity  wind_speed  meanpressure\n",
              "date                                                      \n",
              "2016-12-27  17.348792  70.642850    7.421823   1114.035254\n",
              "2016-12-28  17.726869  68.599848    6.255075   1010.853957\n",
              "2016-12-29  17.496228  70.909252    6.333213    992.402101\n",
              "2016-12-30  17.648930  72.704921    5.795082   1004.788372\n",
              "2016-12-31  17.642094  73.481324    5.946337   1031.434268\n",
              "2017-01-01  17.884142  72.291600    5.717422   1026.203648"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xxw8S9kBKs_F",
        "outputId": "eb948bda-5b85-4501-c914-69d56707cddf"
      },
      "source": [
        "# Check performance of the predictions’ model\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "for i in time_series_train.columns:\n",
        "    print(f'MAE of {i} is {mean_absolute_error(Y[[i]],train_forecast[[i]])}')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE of meantemp is 2.8822831810975678\n",
            "MAE of humidity is 13.130988743455282\n",
            "MAE of wind_speed is 1.9202180017101635\n",
            "MAE of meanpressure is 27.450580288019314\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "QAXXBBPwKv_w",
        "outputId": "58ce3982-240e-4ff2-d8ff-cf7bef6f27ad"
      },
      "source": [
        "# Humidity and Meanpressure are showing higher errors of forecast. We could assume that certain external factors are causing this.\n",
        "# This model, therefore, forecasts wind speed and mean temperature accurately with less than 5% error\n",
        "# Let us now implement this on the test data and forecast for the next 6 future periods of time\n",
        "\n",
        "test_forecast = pd.read_csv('https://raw.githubusercontent.com/rjrahul24/ai-with-python-series/main/05.%20Build%20Concrete%20Time%20Series%20Forecasts/Data/DailyDelhiClimateTrain.csv',parse_dates=['date'], index_col='date')\n",
        "period_range = pd.date_range('2017-01-05',periods=6)\n",
        "order_lag_b = fit_model.k_ar\n",
        "X1,Y1 = test_forecast[1:-order_lag_b],test_forecast[-order_lag_b:]\n",
        "input_val = Y1.values[-order_lag_b:]\n",
        "data_forecast = fit_model.forecast(input_val,steps=order_lag_b)\n",
        "df_forecast = DataFrame(data_forecast,columns=X1.columns,index=period_range)\n",
        "df_forecast"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meantemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>meanpressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>11.812454</td>\n",
              "      <td>90.399783</td>\n",
              "      <td>2.204027</td>\n",
              "      <td>1011.654269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-06</th>\n",
              "      <td>12.599031</td>\n",
              "      <td>87.549017</td>\n",
              "      <td>3.059740</td>\n",
              "      <td>1054.964333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-07</th>\n",
              "      <td>13.324675</td>\n",
              "      <td>86.189030</td>\n",
              "      <td>3.292592</td>\n",
              "      <td>999.169436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-08</th>\n",
              "      <td>13.479389</td>\n",
              "      <td>85.379127</td>\n",
              "      <td>4.482376</td>\n",
              "      <td>1006.623997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-09</th>\n",
              "      <td>13.596384</td>\n",
              "      <td>84.584311</td>\n",
              "      <td>4.302307</td>\n",
              "      <td>1011.046202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-10</th>\n",
              "      <td>13.284262</td>\n",
              "      <td>84.708809</td>\n",
              "      <td>4.491213</td>\n",
              "      <td>1011.668326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             meantemp   humidity  wind_speed  meanpressure\n",
              "2017-01-05  11.812454  90.399783    2.204027   1011.654269\n",
              "2017-01-06  12.599031  87.549017    3.059740   1054.964333\n",
              "2017-01-07  13.324675  86.189030    3.292592    999.169436\n",
              "2017-01-08  13.479389  85.379127    4.482376   1006.623997\n",
              "2017-01-09  13.596384  84.584311    4.302307   1011.046202\n",
              "2017-01-10  13.284262  84.708809    4.491213   1011.668326"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "4I3N5HsBKz4X",
        "outputId": "f15e1c02-f603-4d08-9d8a-72502c6b1706"
      },
      "source": [
        "# Plotting the test data with auto correlation\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "# The next 6 periods of mean temperature (graph 1) and wind_speed (graph 2)\n",
        "plot_acf(df_forecast[\"meantemp\"])\n",
        "plot_acf(df_forecast[\"wind_speed\"])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "9hT7CBF6K2Un",
        "outputId": "c47a8314-1633-4a06-b799-bf29096e7edc"
      },
      "source": [
        "# Import Granger Causality module from the statsmodels package and use the Chi-Squared test metric\n",
        "from statsmodels.tsa.stattools import grangercausalitytests\n",
        "test_var = time_series.columns\n",
        "lag_max = 12\n",
        "test_type = 'ssr_chi2test'\n",
        "\n",
        "causal_val = DataFrame(np.zeros((len(test_var),len(test_var))),columns=test_var,index=test_var)\n",
        "for a in test_var:\n",
        "    for b in test_var:\n",
        "        c = grangercausalitytests ( time_series [ [b,a] ], maxlag = lag_max, verbose = False)\n",
        "        pred_val = [round ( c [ i +1 ] [0] [test_type] [1], 5 ) for i in range (lag_max) ]\n",
        "        min_value = np.min (pred_val)\n",
        "        causal_val.loc[b,a] = min_value\n",
        "causal_val"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>meantemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>meanpressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>meantemp</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.00382</td>\n",
              "      <td>0.00054</td>\n",
              "      <td>0.22012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>humidity</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.13319</td>\n",
              "      <td>0.15053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wind_speed</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.52294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meanpressure</th>\n",
              "      <td>0.00633</td>\n",
              "      <td>0.43641</td>\n",
              "      <td>0.04252</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              meantemp  humidity  wind_speed  meanpressure\n",
              "meantemp       1.00000   0.00382     0.00054       0.22012\n",
              "humidity       0.00000   1.00000     0.13319       0.15053\n",
              "wind_speed     0.00000   0.00000     1.00000       0.52294\n",
              "meanpressure   0.00633   0.43641     0.04252       1.00000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIYnsab7PfY9",
        "outputId": "97c0631f-b1dc-4233-dba9-949c0c157a36"
      },
      "source": [
        "pip install pmdarima"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pmdarima in /usr/local/lib/python3.7/dist-packages (1.8.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.0.1)\n",
            "Requirement already satisfied: numpy~=1.19.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.19.5)\n",
            "Requirement already satisfied: Cython!=0.29.18,>=0.29 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.29.23)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.22.2.post1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.24.3)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (57.2.0)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.1.5)\n",
            "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.12.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BHVjQYt_Tylj"
      },
      "source": [
        "Let us now use the PMDARIMA Methodology to implement Predictions for the Wind Speed Values within our Time Series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiRzCpsYK7FM"
      },
      "source": [
        "from pmdarima.arima import auto_arima\n",
        "from pmdarima.arima import ADFTest\n",
        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
        "from statsmodels.tsa.arima_model import ARIMA"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c13v5fBPRRly",
        "outputId": "c4164c7e-df1a-44cf-994e-29a9e2431969"
      },
      "source": [
        "adf_test=ADFTest(alpha=0.05)\n",
        "adf_test.should_diff(time_series_train[\"wind_speed\"])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.01, False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmmGTxG7PdFi",
        "outputId": "823e43c5-c361-4f0b-fbd2-4d1d2784dd4f"
      },
      "source": [
        "df = time_series_train[\"wind_speed\"]\n",
        "df.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1462,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "zmPD_OPGQpNi",
        "outputId": "20bc889d-3583-4f14-b52a-b936b00df5a1"
      },
      "source": [
        "train=df[:1300]\n",
        "test=df[-250:]\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "plt.plot(train)\n",
        "plt.plot(test)\n",
        "plt.autoscale()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrC5sLGpGfTc"
      },
      "source": [
        "# Reducing size of data to run easily on Colab. Please run the entire dataset on a system with very high GPU Performance.\n",
        "train=df[:100]\n",
        "test=df[-30:]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGgZBiveSyqS",
        "outputId": "c876d28f-21a8-4c3b-9710-a14a057700e6"
      },
      "source": [
        "model=auto_arima(train,start_p=0,d=1,start_q=0,\n",
        "          max_p=5,max_d=5,max_q=5, start_P=0,\n",
        "          D=1, start_Q=0, max_P=5,max_D=5,\n",
        "          max_Q=5, m=12, seasonal=True,\n",
        "          error_action='warn',trace=True,\n",
        "          supress_warnings=True,stepwise=True,\n",
        "          random_state=20,n_fits=50)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(0,1,0)(0,1,0)[12]             : AIC=580.835, Time=0.05 sec\n",
            " ARIMA(1,1,0)(1,1,0)[12]             : AIC=537.279, Time=0.12 sec\n",
            " ARIMA(0,1,1)(0,1,1)[12]             : AIC=inf, Time=0.45 sec\n",
            " ARIMA(1,1,0)(0,1,0)[12]             : AIC=570.863, Time=0.03 sec\n",
            " ARIMA(1,1,0)(2,1,0)[12]             : AIC=537.289, Time=0.29 sec\n",
            " ARIMA(1,1,0)(1,1,1)[12]             : AIC=529.770, Time=0.28 sec\n",
            " ARIMA(1,1,0)(0,1,1)[12]             : AIC=inf, Time=0.38 sec\n",
            " ARIMA(1,1,0)(2,1,1)[12]             : AIC=inf, Time=1.46 sec\n",
            " ARIMA(1,1,0)(1,1,2)[12]             : AIC=inf, Time=2.00 sec\n",
            " ARIMA(1,1,0)(0,1,2)[12]             : AIC=529.981, Time=0.51 sec\n",
            " ARIMA(1,1,0)(2,1,2)[12]             : AIC=inf, Time=2.18 sec\n",
            " ARIMA(0,1,0)(1,1,1)[12]             : AIC=537.408, Time=0.23 sec\n",
            " ARIMA(2,1,0)(1,1,1)[12]             : AIC=521.073, Time=0.44 sec\n",
            " ARIMA(2,1,0)(0,1,1)[12]             : AIC=inf, Time=0.53 sec\n",
            " ARIMA(2,1,0)(1,1,0)[12]             : AIC=530.100, Time=0.14 sec\n",
            " ARIMA(2,1,0)(2,1,1)[12]             : AIC=inf, Time=2.73 sec\n",
            " ARIMA(2,1,0)(1,1,2)[12]             : AIC=inf, Time=2.25 sec\n",
            " ARIMA(2,1,0)(0,1,0)[12]             : AIC=561.200, Time=0.06 sec\n",
            " ARIMA(2,1,0)(0,1,2)[12]             : AIC=521.205, Time=0.65 sec\n",
            " ARIMA(2,1,0)(2,1,0)[12]             : AIC=529.105, Time=0.39 sec\n",
            " ARIMA(2,1,0)(2,1,2)[12]             : AIC=inf, Time=2.98 sec\n",
            " ARIMA(3,1,0)(1,1,1)[12]             : AIC=522.629, Time=0.54 sec\n",
            " ARIMA(2,1,1)(1,1,1)[12]             : AIC=inf, Time=1.16 sec\n",
            " ARIMA(1,1,1)(1,1,1)[12]             : AIC=inf, Time=0.87 sec\n",
            " ARIMA(3,1,1)(1,1,1)[12]             : AIC=inf, Time=1.25 sec\n",
            " ARIMA(2,1,0)(1,1,1)[12] intercept   : AIC=522.875, Time=0.58 sec\n",
            "\n",
            "Best model:  ARIMA(2,1,0)(1,1,1)[12]          \n",
            "Total fit time: 22.597 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KoxYYA57TimT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "273f577a-1890-4c35-d257-f1cad30952fd"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>                   <td>y</td>                <th>  No. Observations:  </th>    <td>100</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(2, 1, 0)x(1, 1, [1], 12)</td> <th>  Log Likelihood     </th> <td>-255.537</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                    <td>Sat, 17 Jul 2021</td>         <th>  AIC                </th>  <td>521.073</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                        <td>06:15:47</td>             <th>  BIC                </th>  <td>533.403</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                          <td>0</td>                <th>  HQIC               </th>  <td>526.038</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                              <td> - 100</td>              <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>                <td>opg</td>               <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>    <td>   -0.4418</td> <td>    0.099</td> <td>   -4.469</td> <td> 0.000</td> <td>   -0.636</td> <td>   -0.248</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2</th>    <td>   -0.3437</td> <td>    0.098</td> <td>   -3.510</td> <td> 0.000</td> <td>   -0.536</td> <td>   -0.152</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.S.L12</th> <td>   -0.1412</td> <td>    0.151</td> <td>   -0.935</td> <td> 0.350</td> <td>   -0.437</td> <td>    0.155</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.S.L12</th> <td>   -0.8371</td> <td>    0.263</td> <td>   -3.182</td> <td> 0.001</td> <td>   -1.353</td> <td>   -0.322</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>   <td>   17.1272</td> <td>    3.609</td> <td>    4.746</td> <td> 0.000</td> <td>   10.054</td> <td>   24.200</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.16</td> <th>  Jarque-Bera (JB):  </th> <td>1.33</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.69</td> <th>  Prob(JB):          </th> <td>0.51</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>0.96</td> <th>  Skew:              </th> <td>0.30</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.92</td> <th>  Kurtosis:          </th> <td>3.10</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                      SARIMAX Results                                       \n",
              "============================================================================================\n",
              "Dep. Variable:                                    y   No. Observations:                  100\n",
              "Model:             SARIMAX(2, 1, 0)x(1, 1, [1], 12)   Log Likelihood                -255.537\n",
              "Date:                              Sat, 17 Jul 2021   AIC                            521.073\n",
              "Time:                                      06:15:47   BIC                            533.403\n",
              "Sample:                                           0   HQIC                           526.038\n",
              "                                              - 100                                         \n",
              "Covariance Type:                                opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1         -0.4418      0.099     -4.469      0.000      -0.636      -0.248\n",
              "ar.L2         -0.3437      0.098     -3.510      0.000      -0.536      -0.152\n",
              "ar.S.L12      -0.1412      0.151     -0.935      0.350      -0.437       0.155\n",
              "ma.S.L12      -0.8371      0.263     -3.182      0.001      -1.353      -0.322\n",
              "sigma2        17.1272      3.609      4.746      0.000      10.054      24.200\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.16   Jarque-Bera (JB):                 1.33\n",
              "Prob(Q):                              0.69   Prob(JB):                         0.51\n",
              "Heteroskedasticity (H):               0.96   Skew:                             0.30\n",
              "Prob(H) (two-sided):                  0.92   Kurtosis:                         3.10\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LpYFZchgdhqr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9ddbf2e7-2c4f-478e-a5c1-7c1d1b81ca51"
      },
      "source": [
        "prediction = pd.DataFrame(model.predict(n_periods = 30),index=test.index)\n",
        "prediction.columns = ['wind_Speed_preds']\n",
        "prediction"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>wind_Speed_preds</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-12-03</th>\n",
              "      <td>8.682382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-04</th>\n",
              "      <td>8.510031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-05</th>\n",
              "      <td>7.433569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-06</th>\n",
              "      <td>7.547177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-07</th>\n",
              "      <td>8.742537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-08</th>\n",
              "      <td>7.178530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-09</th>\n",
              "      <td>10.475130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-10</th>\n",
              "      <td>12.178019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-11</th>\n",
              "      <td>11.673995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-12</th>\n",
              "      <td>7.314988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-13</th>\n",
              "      <td>7.253430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-14</th>\n",
              "      <td>7.042935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-15</th>\n",
              "      <td>10.335278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-16</th>\n",
              "      <td>8.659973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-17</th>\n",
              "      <td>7.714092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-18</th>\n",
              "      <td>8.589048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-19</th>\n",
              "      <td>9.362955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-20</th>\n",
              "      <td>8.459273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-21</th>\n",
              "      <td>11.971072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-22</th>\n",
              "      <td>12.886645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-23</th>\n",
              "      <td>12.224796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-24</th>\n",
              "      <td>7.785947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-25</th>\n",
              "      <td>8.202159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-26</th>\n",
              "      <td>7.658741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-27</th>\n",
              "      <td>10.853353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-28</th>\n",
              "      <td>9.390030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-29</th>\n",
              "      <td>8.424940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-30</th>\n",
              "      <td>9.192826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-31</th>\n",
              "      <td>10.026314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>9.029237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            wind_Speed_preds\n",
              "date                        \n",
              "2016-12-03          8.682382\n",
              "2016-12-04          8.510031\n",
              "2016-12-05          7.433569\n",
              "2016-12-06          7.547177\n",
              "2016-12-07          8.742537\n",
              "2016-12-08          7.178530\n",
              "2016-12-09         10.475130\n",
              "2016-12-10         12.178019\n",
              "2016-12-11         11.673995\n",
              "2016-12-12          7.314988\n",
              "2016-12-13          7.253430\n",
              "2016-12-14          7.042935\n",
              "2016-12-15         10.335278\n",
              "2016-12-16          8.659973\n",
              "2016-12-17          7.714092\n",
              "2016-12-18          8.589048\n",
              "2016-12-19          9.362955\n",
              "2016-12-20          8.459273\n",
              "2016-12-21         11.971072\n",
              "2016-12-22         12.886645\n",
              "2016-12-23         12.224796\n",
              "2016-12-24          7.785947\n",
              "2016-12-25          8.202159\n",
              "2016-12-26          7.658741\n",
              "2016-12-27         10.853353\n",
              "2016-12-28          9.390030\n",
              "2016-12-29          8.424940\n",
              "2016-12-30          9.192826\n",
              "2016-12-31         10.026314\n",
              "2017-01-01          9.029237"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nSrV80HG1ue"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}